{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Implementing IPP in a Simulated GP Environment\n",
    "This notebook is intended for to serve as a GP playground for different informative path planning algorithms.\n",
    "\n",
    "Please note, that you may need to install the following in order to play with the [dubins library](https://github.com/AndrewWalker/Dubins-Curves) and the [Gaussian Process model](https://github.com/SheffieldML/GPy) respectively:\n",
    "\n",
    "```pip install dubins\n",
    "pip install GPy```\n",
    "\n",
    "Also please note that this notebook is DEPRECATED.\n",
    "\n",
    "This library allows for the generation of a Gaussian environment with known kernel parameter and provides an point-robot interface for planning informative paths through this initially unknown environment, while collecting noisy samples of the phenonema of interest from the environment. The robot's trajectories are represented as concatonations of dubins curves, assuming that the turning radius is known. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Necessary imports\n",
    "%matplotlib inline\n",
    "\n",
    "from matplotlib import pyplot as plt\n",
    "import matplotlib\n",
    "from matplotlib.colors import LogNorm\n",
    "from matplotlib import cm\n",
    "from sklearn import mixture\n",
    "from IPython.display import display\n",
    "from scipy.stats import multivariate_normal\n",
    "import numpy as np\n",
    "import math\n",
    "import os\n",
    "import GPy as GPy\n",
    "import dubins\n",
    "import time\n",
    "from itertools import chain"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## The GPModel Class\n",
    "This custom GPModel class is the model representation used to generate a simulated Gaussian environment, and also used to represent the robot's current model of the environment. The class is a wrapper on to of the GPy library that allows for some additional features, including:\n",
    "* Saving and loading trained kernel parameters\n",
    "* Initializing a GP model without any prior data and adding new sample points\n",
    "* Incrementally addding sample points to a previous dataset, resuing previous computation [TODO]\n",
    "* Sparse, streaming GP posterior updates [TODO]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## The Squared Exponential Kernel\n",
    "\n",
    "The GPModel currently assumes a Squared Exponential Kernel. Here is a guide to what the differnt kernel hyperparmeters mean, from [this post](http://evelinag.com/Ariadne/covarianceFunctions.html). \n",
    "\n",
    "\\begin{equation}\n",
    "\\kappa(x_i, x_j) = \\sigma^2 \\text{exp}\\Big(- \\frac{(x_i - x_j)^2}{2l^2}\\Big) + \\sigma_{\\text{noise}}^2\n",
    "\\end{equation} \n",
    "\n",
    "where $\\sigma^2 > 0$ is the signal variance, $l>0$ is the lengthscale and $\\sigma_{\\text{noise}}^2>=0$ is the noise covariance. The noise variance is applied only when $i=j$.\n",
    "\n",
    "Squared exponential is appropriate for modelling very smooth functions. The parameters have the following interpretation:\n",
    "\n",
    "* **Lengthscale $l$** describes how smooth a function is. Small lengthscale value means that function values can change quickly, large values characterize functions that change only slowly. Lengthscale also determines how far we can reliably extrapolate from the training data.\n",
    "\n",
    "* **Signal variance $\\sigma^2$** is a scaling factor. It determines variation of function values from their mean. Small value of $\\sigma^2$ characterize functions that stay close to their mean value, larger values allow more variation. If the signal variance is too large, the modelled function will be free to chase outliers.\n",
    "\n",
    "* **Noise variance** $\\sigma_{\\text{noise}}^2$ is formally not a part of the covariance function itself. It is used by the Gaussian process model to allow for noise present in training data. This parameter specifies how much noise is expected to be present in the data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "class GPModel:\n",
    "    '''The GPModel class, which is a wrapper on top of GPy, allowing saving and loading of trained kernel parameters.\n",
    "    Inputs:\n",
    "    * variance (float) the variance parameter of the squared exponential kernel\n",
    "    * lengthscale (float) the lengthscale parameter of the squared exponential kernel\n",
    "    * noise (float) the sensor noise parameter of the squared exponential kernel\n",
    "    * dimension (float) the dimension of the environment (currently, only 2D environments are supported)\n",
    "    * kernel (string) the type of kernel (currently, only 'rbf' kernels are supported) '''     \n",
    "    \n",
    "    def __init__(self, lengthscale, variance, noise = 0.05, dimension = 2, kernel = 'rbf'):\n",
    "        '''Initialize a GP regression model with given kernel parameters. '''\n",
    "        \n",
    "        # The noise parameter of the sensor\n",
    "        self.noise = noise\n",
    "        self.lengthscale = lengthscale\n",
    "        self.variance = variance\n",
    "        \n",
    "        # The Gaussian dataset\n",
    "        self.xvals = None\n",
    "        self.zvals = None\n",
    "        \n",
    "        # The dimension of the evironment\n",
    "        if dimension == 2:\n",
    "            self.dim = dimension\n",
    "        else:\n",
    "            raise ValueError('Environment must have dimension 2 \\'rbf\\'')\n",
    "\n",
    "        if kernel == 'rbf':\n",
    "            self.kern = GPy.kern.RBF(input_dim = self.dim, lengthscale = lengthscale, variance = variance) \n",
    "        else:\n",
    "            raise ValueError('Kernel type must by \\'rbf\\'')\n",
    "            \n",
    "        # Intitally, before any data is created, \n",
    "        self.model = None\n",
    "         \n",
    "    def predict_value(self, xvals):\n",
    "        ''' Public method returns the mean and variance predictions at a set of input locations.\n",
    "        Inputs:\n",
    "        * xvals (float array): an nparray of floats representing observation locations, with dimension NUM_PTS x 2\n",
    "        \n",
    "        Returns: \n",
    "        * mean (float array): an nparray of floats representing predictive mean, with dimension NUM_PTS x 1         \n",
    "        * var (float array): an nparray of floats representing predictive variance, with dimension NUM_PTS x 1 '''        \n",
    "\n",
    "        assert(xvals.shape[0] >= 1)            \n",
    "        assert(xvals.shape[1] == self.dim)    \n",
    "        \n",
    "        n_points, input_dim = xvals.shape\n",
    "        \n",
    "        # With no observations, predict 0 mean everywhere and prior variance\n",
    "        if self.model == None:\n",
    "            return np.zeros((n_points, 1)), np.ones((n_points, 1)) * self.variance\n",
    "        \n",
    "        # Else, return \n",
    "        mean, var = self.model.predict(xvals, full_cov = False, include_likelihood = True)\n",
    "        return mean, var        \n",
    "    \n",
    "\n",
    "    def set_data(self, xvals, zvals):\n",
    "        ''' Public method that updates the data in the GP model.\n",
    "        Inputs:\n",
    "        * xvals (float array): an nparray of floats representing observation locations, with dimension NUM_PTS x 2\n",
    "        * zvals (float array): an nparray of floats representing sensor observations, with dimension NUM_PTS x 1 ''' \n",
    "        \n",
    "        # Save the data internally\n",
    "        self.xvals = xvals\n",
    "        self.zvals = zvals\n",
    "        \n",
    "        # If the model hasn't been created yet (can't be created until we have data), create GPy model\n",
    "        if self.model == None:\n",
    "            self.model = GPy.models.GPRegression(np.array(xvals), np.array(zvals), self.kern)\n",
    "        # Else add to the exisiting model\n",
    "        else:\n",
    "            self.model.set_XY(X = np.array(xvals), Y = np.array(zvals))\n",
    "    \n",
    "    def add_data(self, xvals, zvals):\n",
    "        ''' Public method that adds data to an the GP model.\n",
    "        Inputs:\n",
    "        * xvals (float array): an nparray of floats representing observation locations, with dimension NUM_PTS x 2\n",
    "        * zvals (float array): an nparray of floats representing sensor observations, with dimension NUM_PTS x 1 ''' \n",
    "        \n",
    "        if self.xvals is None:\n",
    "            self.xvals = xvals\n",
    "        else:\n",
    "            self.xvals = np.vstack([self.xvals, xvals])\n",
    "            \n",
    "        if self.zvals is None:\n",
    "            self.zvals = zvals\n",
    "        else:\n",
    "            self.zvals = np.vstack([self.zvals, zvals])\n",
    "\n",
    "        # If the model hasn't been created yet (can't be created until we have data), create GPy model\n",
    "        if self.model == None:\n",
    "            self.model = GPy.models.GPRegression(np.array(xvals), np.array(zvals), self.kern)\n",
    "#             self.model.optimize()\n",
    "        # Else add to the exisiting model\n",
    "        else:\n",
    "            self.model.set_XY(X = np.array(self.xvals), Y = np.array(self.zvals))\n",
    "#             self.model.optimize()\n",
    "\n",
    "    def load_kernel(self, kernel_file = 'kernel_model.npy'):\n",
    "        ''' Public method that loads kernel parameters from file.\n",
    "        Inputs:\n",
    "        * kernel_file (string): a filename string with the location of the kernel parameters '''    \n",
    "        \n",
    "        # Read pre-trained kernel parameters from file, if avaliable and no training data is provided\n",
    "        if os.path.isfile(kernel_file):\n",
    "            print \"Loading kernel parameters from file\"\n",
    "            self.kern[:] = np.load(kernel_file)\n",
    "        else:\n",
    "            raise ValueError(\"Failed to load kernel. Kernel parameter file not found.\")\n",
    "            \n",
    "        return\n",
    "\n",
    "    def train_kernel(self, xvals = None, zvals = None, kernel_file = 'kernel_model.npy'):\n",
    "        ''' Public method that optmizes kernel parameters based on input data and saves to files.\n",
    "        Inputs:\n",
    "        * xvals (float array): an nparray of floats representing observation locations, with dimension NUM_PTS x 2\n",
    "        * zvals (float array): an nparray of floats representing sensor observations, with dimension NUM_PTS x 1        \n",
    "        * kernel_file (string): a filename string with the location to save the kernel parameters '''      \n",
    "        \n",
    "        # Read pre-trained kernel parameters from file, if avaliable and no training data is provided\n",
    "        if xvals is not None and zvals is not None:\n",
    "            print \"Optimizing kernel parameters given data\"\n",
    "            # Initilaize a GP model (used only for optmizing kernel hyperparamters)\n",
    "            self.m = GPy.models.GPRegression(np.array(xvals), np.array(zvals), self.kern)\n",
    "            self.m.initialize_parameter()\n",
    "\n",
    "            # Constrain the hyperparameters during optmization\n",
    "            self.m.constrain_positive('')\n",
    "            #self.m['rbf.variance'].constrain_bounded(0.01, 10)\n",
    "            #self.m['rbf.lengthscale'].constrain_bounded(0.01, 10)\n",
    "            self.m['Gaussian_noise.variance'].constrain_fixed(self.noise)\n",
    "\n",
    "            # Train the kernel hyperparameters\n",
    "            self.m.optimize_restarts(num_restarts = 2, messages = True)\n",
    "\n",
    "            # Save the hyperparemters to file\n",
    "            np.save(kernel_file, self.kern[:])\n",
    "        else:\n",
    "            raise ValueError(\"Failed to train kernel. No training data provided.\")\n",
    "            \n",
    "    def visualize_model(self, x1lim, x2lim, title = ''):\n",
    "        if self.model is None:\n",
    "            print 'No samples have been collected. World model is equivalent to prior.'\n",
    "            return None\n",
    "        else:\n",
    "            print \"Sample set size:\", self.xvals.shape\n",
    "            fig = self.model.plot(figsize=(4, 3), title = title, xlim = x1lim, ylim = x2lim)\n",
    "            \n",
    "    def kernel_plot(self):\n",
    "        ''' Visualize the learned GP kernel '''        \n",
    "        _ = self.kern.plot()\n",
    "        plt.ylim([-10, 10])\n",
    "        plt.xlim([-10, 10])\n",
    "        plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## The Environment Class\n",
    "This custom Environment class is used to represent environments of interest. Currently, the enviroment is constrained to be a rectangular region in $\\mathbb{R}^2$. Upon initialization, the Environment class draws a random function from a Gaussian Process with the input covariance function. The Environment has one public method, ``sample_value``, which returns a noisy sample of the function value at a specified location. \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "class Environment:\n",
    "    '''The Environment class, which represents a retangular Gaussian world.\n",
    "    \n",
    "    Input:\n",
    "    * ranges (tuple of floats): a tuple representing the max/min of 2D rectangular domain i.e. (-10, 10, -50, 50)\n",
    "    * NUM_PTS (int): the number of points in each dimension to sample for initialization, \n",
    "                    resulting in a sample grid of size NUM_PTS x NUM_PTS\n",
    "    * variance (float): the variance parameter of the squared exponential kernel\n",
    "    * lengthscale (float): the lengthscale parameter of the squared exponential kernel\n",
    "    * noise (float): the sensor noise parameter of the squared exponential kernel\n",
    "    * visualize (boolean): a boolean flag to plot the surface of the resulting environment \n",
    "    * seed (int): an integer seed for the random draws. If set to \\'None\\', no seed is used ''' \n",
    "\n",
    "    def __init__(self, ranges, NUM_PTS, variance = 0.5, lengthscale = 1.0, noise = 0.05, visualize = True, seed = None, dim = 2):\n",
    "        ''' Initialize a random Gaussian environment using the input kernel, assuming zero mean'''\n",
    "        # Save the parmeters of GP model\n",
    "        self.variance = variance\n",
    "        self.lengthscale = lengthscale\n",
    "        self.dim = dim\n",
    "        \n",
    "        # Expect ranges to be a 4-tuple consisting of x1min, x1max, x2min, and x2max\n",
    "        self.x1min = float(ranges[0])\n",
    "        self.x1max = float(ranges[1])\n",
    "        self.x2min = float(ranges[2])\n",
    "        self.x2max = float(ranges[3]) \n",
    "        \n",
    "        # Intialize a GP model of the environment\n",
    "        self.GP = GPModel(lengthscale = lengthscale, variance = variance)         \n",
    "                            \n",
    "        # Generate a set of discrete grid points, uniformly spread across the environment\n",
    "        x1 = np.linspace(self.x1min, self.x1max, NUM_PTS)\n",
    "        x2 = np.linspace(self.x2min, self.x2max, NUM_PTS)\n",
    "        x1vals, x2vals = np.meshgrid(x1, x2, sparse = False, indexing = 'xy') # dimension: NUM_PTS x NUM_PTS\n",
    "        data = np.vstack([x1vals.ravel(), x2vals.ravel()]).T # dimension: NUM_PTS*NUM_PTS x 2\n",
    "\n",
    "        # Take an initial sample in the GP prior, conditioned on no other data\n",
    "        xsamples = np.reshape(np.array(data[0, :]), (1, dim)) # dimension: 1 x 2        \n",
    "        mean, var = self.GP.predict_value(xsamples)   \n",
    "        \n",
    "        if seed is not None:\n",
    "            np.random.seed(seed)\n",
    "            seed += 1\n",
    "        zsamples = np.random.normal(loc = mean, scale = np.sqrt(var))\n",
    "        zsamples = np.reshape(zsamples, (1,1)) # dimension: 1 x 1 \n",
    "                            \n",
    "        # Add new data point to the GP model\n",
    "        self.GP.set_data(xsamples, zsamples)                            \n",
    "                                 \n",
    "        # Iterate through the rest of the grid sequentially and sample a z values, condidtioned on previous samples\n",
    "        for index, point in enumerate(data[1:, :]):\n",
    "            # Get a new sample point\n",
    "            xs = np.reshape(np.array(point), (1, dim))\n",
    "    \n",
    "            # Compute the predicted mean and variance\n",
    "            mean, var = self.GP.predict_value(xs)\n",
    "            \n",
    "            # Sample a new observation, given the mean and variance\n",
    "            if seed is not None:\n",
    "                np.random.seed(seed)\n",
    "                seed += 1            \n",
    "            zs = np.random.normal(loc = mean, scale = np.sqrt(var))\n",
    "            \n",
    "            # Add new sample point to the GP model\n",
    "            zsamples = np.vstack([zsamples, np.reshape(zs, (1, 1))])\n",
    "            xsamples = np.vstack([xsamples, np.reshape(xs, (1, dim))])\n",
    "            self.GP.set_data(xsamples, zsamples)\n",
    "      \n",
    "        # Plot the surface mesh and scatter plot representation of the samples points\n",
    "        if visualize == True:\n",
    "            fig = plt.figure(figsize=(4, 3))\n",
    "            ax = fig.add_subplot(111, projection = '3d')\n",
    "            ax.set_title('Surface of the Simulated Environment')\n",
    "            surf = ax.plot_surface(x1vals, x2vals, zsamples.reshape(x1vals.shape), cmap = cm.coolwarm, linewidth = 1)\n",
    "\n",
    "            #ax2 = fig.add_subplot(212, projection = '3d')\n",
    "            \n",
    "            fig2 = plt.figure(figsize=(4, 3))\n",
    "            ax2 = fig2.add_subplot(111)\n",
    "            ax2.set_title('Countour Plot of the Simulated Environment')     \n",
    "            plot = ax2.contourf(x1vals, x2vals, zsamples.reshape(x1vals.shape), cmap = 'viridis')\n",
    "            scatter = ax2.scatter(data[:, 0], data[:, 1], c = zsamples.ravel(), s = 4.0, cmap = 'viridis')            \n",
    "            plt.show()           \n",
    "        \n",
    "        print \"Environment initialized with bounds X1: (\", self.x1min, \",\", self.x1max, \")  X2:(\", self.x2min, \",\", self.x2max, \")\" \n",
    "      \n",
    "    def sample_value(self, xvals):\n",
    "        ''' The public interface to the Environment class. Returns a noisy sample of the true value of environment \n",
    "        at a set of point. \n",
    "        Input:\n",
    "        * xvals (float array): an nparray of floats representing observation locations, with dimension NUM_PTS x 2 \n",
    "        \n",
    "        Returns:\n",
    "        * mean (float array): an nparray of floats representing predictive mean, with dimension NUM_PTS x 1 '''\n",
    "        assert(xvals.shape[0] >= 1)            \n",
    "        assert(xvals.shape[1] == self.dim)        \n",
    "        mean, var = self.GP.predict_value(xvals)\n",
    "        return mean\n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Representing Path Sets: Path Generator Classes\n",
    "This custom class leverages some of the nice features of the ```dubins``` library to generate a number of options and sample sets for the vehicle to choose from."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "class Path_Generator:\n",
    "    '''The Path_Generator class which creates naive point-to-point straightline paths'''\n",
    "    \n",
    "    def __init__(self, frontier_size, horizon_length, turning_radius, sample_step, extent):\n",
    "        '''\n",
    "        frontier_size (int) the number of points on the frontier we should consider for navigation\n",
    "        horizon_length (float) distance between the vehicle and the horizon to consider\n",
    "        turning_radius (float) the feasible turning radius for the vehicle\n",
    "        sample_step (float) the unit length along the path from which to draw a sample\n",
    "        '''\n",
    "\n",
    "        self.fs = frontier_size\n",
    "        self.hl = horizon_length\n",
    "        self.tr = turning_radius\n",
    "        self.ss = sample_step\n",
    "        self.extent = extent\n",
    "\n",
    "        # Global variables\n",
    "        self.goals = [] #The frontier coordinates\n",
    "        self.samples = {} #The sample points which form the paths\n",
    "        self.cp = (0,0,0) #The current pose of the vehicle\n",
    "        \n",
    "    def generate_frontier_points(self):\n",
    "        '''From the frontier_size and horizon_length, generate the frontier points to goal'''\n",
    "        angle = np.linspace(-2.35,2.35,self.fs) #fix the possibilities to 75% of the unit circle, ignoring points directly behind the vehicle\n",
    "        goals = [(self.hl*np.cos(self.cp[2]+a)+self.cp[0], self.hl*np.sin(self.cp[2]+a)+self.cp[1], self.cp[2]+a) for a in angle]\n",
    "        self.goals = goals#[coordinate for coordinate in goals if coordinate[0] < self.extent[1] and coordinate[0] > self.extent[0] and coordinate[1] < self.extent[3] and coordinate[1] > self.extent[2]]\n",
    "        return self.goals\n",
    "        \n",
    "    def make_sample_paths(self):\n",
    "        '''Connect the current_pose to the goal places'''\n",
    "        cp = np.array(self.cp)\n",
    "        coords = {}\n",
    "        for i,goal in enumerate(self.goals):\n",
    "            g = np.array(goal)\n",
    "            distance = np.sqrt((cp[0]-g[0])**2 + (cp[1]-g[1])**2)\n",
    "            samples = int(round(distance/self.ss))\n",
    "            \n",
    "            # Don't include the start location but do include the end point\n",
    "            for j in range(0,samples):\n",
    "                x = cp[0]+((j+1)*self.ss)*np.cos(g[2])\n",
    "                y = cp[1]+((j+1)*self.ss)*np.sin(g[2])\n",
    "                a = g[2]\n",
    "                try: \n",
    "                    coords[i].append((x,y,a))\n",
    "                except:\n",
    "                    coords[i] = []\n",
    "                    coords[i].append((x,y,a))\n",
    "        self.samples = coords\n",
    "        return coords\n",
    "    \n",
    "    def get_path_set(self, current_pose):\n",
    "        '''Primary interface for getting list of path sample points for evaluation'''\n",
    "        self.cp = current_pose\n",
    "        self.generate_frontier_points()\n",
    "        paths = self.make_sample_paths()\n",
    "        return paths\n",
    "    \n",
    "    def get_frontier_points(self):\n",
    "        return self.goals\n",
    "    \n",
    "    def get_sample_points(self):\n",
    "        return self.samples"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "class Dubins_Path_Generator(Path_Generator):\n",
    "    '''\n",
    "    The Dubins_Path_Generator class, which inherits from the Path_Generator class. Replaces the make_sample_paths\n",
    "    method with paths generated using the dubins library\n",
    "    '''\n",
    "        \n",
    "    def make_sample_paths(self):\n",
    "        '''Connect the current_pose to the goal places'''\n",
    "        coords = {}\n",
    "        for i,goal in enumerate(self.goals):\n",
    "            g = (goal[0],goal[1],self.cp[2])\n",
    "            path = dubins.shortest_path(self.cp, goal, self.tr)\n",
    "            configurations, _ = path.sample_many(self.ss)\n",
    "            coords[i] = [config for config in configurations if config[0] > self.extent[0] and config[0] < self.extent[1] and config[1] > self.extent[2] and config[1] < self.extent[3]]\n",
    "        \n",
    "        self.samples = coords\n",
    "        return coords"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "class Dubins_EqualPath_Generator(Path_Generator):\n",
    "    '''\n",
    "    The Dubins_EqualPath_Generator class which inherits from Path_Generator. Modifies Dubin Curve paths so that all\n",
    "    options have an equal number of sampling points\n",
    "    '''\n",
    "        \n",
    "    def make_sample_paths(self):\n",
    "        '''Connect the current_pose to the goal places'''\n",
    "        coords = {}\n",
    "        for i,goal in enumerate(self.goals):\n",
    "            g = (goal[0],goal[1],self.cp[2])\n",
    "            path = dubins.shortest_path(self.cp, goal, self.tr)\n",
    "            configurations, _ = path.sample_many(self.ss)\n",
    "            coords[i] = [config for config in configurations if config[0] > self.extent[0] and config[0] < self.extent[1] and config[1] > self.extent[2] and config[1] < self.extent[3]]\n",
    "        \n",
    "        # find the \"shortest\" path in sample space\n",
    "        current_min = 1000\n",
    "        for key,path in coords.items():\n",
    "            if len(path) < current_min and len(path) > 1:\n",
    "                current_min = len(path)\n",
    "        \n",
    "        # limit all paths to the shortest path in sample space\n",
    "        for key,path in coords.items():\n",
    "            if len(path) > current_min:\n",
    "                path = path[0:current_min]\n",
    "                coords[key]=path\n",
    "        \n",
    "        self.samples = coords\n",
    "        return coords"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## The Evaluation Class\n",
    "This custom Evaluation class store performance metrics for the robot during mission execution. It is initialized with a ground truth world model, which is used only for the purpous of real-time performance tracking. The Evaluation class includes a variety of differnt evaluation metrics.\n",
    "\n",
    "### The Model: \n",
    "We have some ground truth scalar, stationary function $m: \\mathbb{X} \\to \\mathbb{R}$ e.g. methane concentration, temperature, etc. We place a GP prior on the unknown function $m$ with mean $\\mu$ and covariance function $\\kappa$:\n",
    "$$ m \\sim \\mathcal{GP}(\\mu, \\kappa) $$\n",
    "\n",
    "We define a reward function $f: 2^{\\mathbb{X}} \\to \\mathbb{R}$ that quantifies the true reward of a robot being in a set of states $\\mathcal{A} \\subset \\mathbb{X}$:\n",
    "$$ f(\\mathcal{A}) = IG(m; \\mathcal{A}) + \\lambda \\sum_{\\mathbf{x} \\in \\mathcal{A}} m(\\mathbf{x}) $$\n",
    "where $IG: 2^{\\mathbb{X}} \\to \\mathbb{R}$ is the information gain of a set of points with respect to the underlying function $m$.\n",
    "\n",
    "We also define an aquisition function $\\alpha: 2^{\\mathbb{X}} \\to \\mathbb{R}$, by which the robot can select promising trajectories:\n",
    "$$ \\alpha(\\mathcal{A}) = f(\\mathcal{A}) + \\beta_t \\sum_{\\mathbf{x} \\in \\mathcal{A}} \\sigma_{t-1}(\\mathbf{x})$$\n",
    "$$ \\alpha(\\mathcal{A}) = IG(m; \\mathcal{A}) + \\lambda \\sum_{\\mathbf{x} \\in \\mathcal{A}} \\mu_{t-1}(\\mathbf{x}) + \\beta_t \\lambda \\sum_{\\mathbf{x} \\in \\mathcal{A}} \\sigma_{t-1}(\\mathbf{x})$$\n",
    "\n",
    "Here is an outline of the metrics:\n",
    "\n",
    "* Information Gain $\\mathcal{I}(m; \\mathbf{y}_A \\mid \\mathbf{y}_{obs})$: The mutual information between a set of potential observations $\\mathbf{y}_A$ and the underlying function $m$, given a set of previous observations $\\mathbf{y}_{obs}$.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "class Evaluation:\n",
    "    def __init__(self, world, reward_function = 'mean'):\n",
    "        self.world = world\n",
    "        \n",
    "        self.metrics = {'aquisition_function': {},\n",
    "                        'mean_reward': {}, \n",
    "                        'info_gain_reward': {},                         \n",
    "                        'hotspot_info_reward': {}, \n",
    "                        'MSE': {},                         \n",
    "                        'instant_regret': {},                        \n",
    "                       }\n",
    "        \n",
    "        self.reward_function = reward_function\n",
    "        \n",
    "        if reward_function == 'hotspot_info':\n",
    "            self.f_rew = self.hotspot_info_reward\n",
    "            self.f_aqu = hotspot_info_UCB\n",
    "        elif reward_function == 'mean':\n",
    "            self.f_rew = self.mean_reward\n",
    "            self.f_aqu = mean_UCB      \n",
    "        elif reward_function == 'info_gain':\n",
    "            self.f_rew = self.info_gain_reward\n",
    "            self.f_aqu = info_gain             \n",
    "        else:\n",
    "            raise ValueError('Only \\'mean\\' and \\'hotspot_info\\' reward functions currently supported.')    \n",
    "    \n",
    "    ''' %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n",
    "                                Reward Functions - should have the form:\n",
    "    def reward(time, xvals), where:\n",
    "    * time (int): the current timestep of planning\n",
    "    * xvals (list of float tuples): representing a path i.e. [(3.0, 4.0), (5.6, 7.2), ... ])\n",
    "    * robot_model (GPModel)\n",
    "    \n",
    "    %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%'''\n",
    "    def mean_reward(self, time, xvals, robot_model):\n",
    "        ''' Predcited mean (true) reward function'''\n",
    "        data = np.array(xvals)\n",
    "        x1 = data[:,0]\n",
    "        x2 = data[:,1]\n",
    "        queries = np.vstack([x1, x2]).T   \n",
    "        \n",
    "        mu, var = self.world.GP.predict_value(queries)\n",
    "        return np.sum(mu)   \n",
    "\n",
    "\n",
    "    def hotspot_info_reward(self, time, xvals, robot_model):\n",
    "        ''' The reward information gathered plus the exploitation value gathered'''    \n",
    "        LAMBDA = 0.5\n",
    "        data = np.array(xvals)\n",
    "        x1 = data[:,0]\n",
    "        x2 = data[:,1]\n",
    "        queries = np.vstack([x1, x2]).T   \n",
    "        \n",
    "        mu, var = self.world.GP.predict_value(queries)    \n",
    "        return self.info_gain_reward(time, xvals, robot_model) + LAMBDA * np.sum(mu)\n",
    "    \n",
    "    def info_gain_reward(self, time, xvals, robot_model):\n",
    "        ''' The information reward gathered '''\n",
    "        return info_gain(time, xvals, robot_model)\n",
    "    \n",
    "    ''' %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n",
    "                                               End Reward Functions\n",
    "        %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%'''        \n",
    "    def inst_regret(self, t, all_paths, selected_path, robot_model):\n",
    "        ''' The instantaneous Kapoor regret of a selected path, according to the specified reward function\n",
    "        Input:\n",
    "        * all_paths: the set of all avalaible paths to the robot at time t\n",
    "        * selected path: the path selected by the robot at time t '''\n",
    "        \n",
    "        value_omni = {}        \n",
    "        for path, points in all_paths.items():           \n",
    "            value_omni[path] =  self.f_rew(time = t, xvals = points, robot_model = robot_model)  \n",
    "        value_max = value_omni[max(value_omni, key = value_omni.get)]\n",
    "        \n",
    "        value_selected = self.f_rew(time = t, xvals = selected_path, robot_model = robot_model)\n",
    "\n",
    "        #assert(value_max - value_selected >= 0)\n",
    "        return value_max - value_selected\n",
    "        \n",
    "    def MSE(self, robot_model, NTEST = 10):\n",
    "        ''' Compute the MSE on a set of test points, randomly distributed throughout the environment'''\n",
    "        np.random.seed(0)\n",
    "        x1 = np.random.random_sample((NTEST, 1)) * (self.world.x1max - self.world.x1min) + self.world.x1min\n",
    "        x2 = np.random.random_sample((NTEST, 1)) * (self.world.x2max - self.world.x2min) + self.world.x2min\n",
    "        data = np.hstack((x1, x2))\n",
    "        \n",
    "        pred_world, var_world = self.world.GP.predict_value(data)\n",
    "        pred_robot, var_robot = robot_model.predict_value(data)      \n",
    "        \n",
    "        return ((pred_world - pred_robot) ** 2).mean()\n",
    "    \n",
    "    def update_metrics(self, t, robot_model, all_paths, selected_path):\n",
    "        ''' Function to update avaliable metrics'''    \n",
    "        # Compute aquisition function\n",
    "        self.metrics['aquisition_function'][t] = self.f_aqu(t, selected_path, robot_model)\n",
    "        \n",
    "        # Compute reward functions\n",
    "        self.metrics['mean_reward'][t] = self.mean_reward(t, selected_path, robot_model)\n",
    "        self.metrics['info_gain_reward'][t] = self.info_gain_reward(t, selected_path, robot_model)\n",
    "        self.metrics['hotspot_info_reward'][t] = self.hotspot_info_reward(t, selected_path, robot_model)\n",
    "        \n",
    "        # Compute other performance metrics\n",
    "        self.metrics['MSE'][t] = self.MSE(robot_model, NTEST = 25)\n",
    "        self.metrics['instant_regret'][t] = self.inst_regret(t, all_paths, selected_path, robot_model)\n",
    "    \n",
    "    def plot_metrics(self):\n",
    "        # Asumme that all metrics have the same time as MSE; not necessary\n",
    "        time = np.array(self.metrics['MSE'].keys())\n",
    "        \n",
    "        ''' Metrics that require a ground truth global model to compute'''        \n",
    "        MSE = np.array(self.metrics['MSE'].values())\n",
    "        regret = np.cumsum(np.array(self.metrics['instant_regret'].values()))\n",
    "        mean = np.cumsum(np.array(self.metrics['mean_reward'].values()))\n",
    "        hotspot_info = np.cumsum(np.array(self.metrics['hotspot_info_reward'].values()))\n",
    "        \n",
    "        ''' Metrics that the robot can compute online '''\n",
    "        info_gain = np.cumsum(np.array(self.metrics['info_gain_reward'].values()))        \n",
    "        UCB = np.cumsum(np.array(self.metrics['aquisition_function'].values()))\n",
    "        \n",
    "        fig, ax = plt.subplots(figsize=(4, 3))\n",
    "        ax.set_title('Accumulated UCB Aquisition Function')             \n",
    "        plt.plot(time, UCB, 'g')\n",
    "        \n",
    "        fig, ax = plt.subplots(figsize=(4, 3))\n",
    "        ax.set_title('Accumulated Information Gain')                             \n",
    "        plt.plot(time, info_gain, 'k')        \n",
    "        \n",
    "        fig, ax = plt.subplots(figsize=(4, 3))\n",
    "        ax.set_title('Accumulated Mean Reward')                     \n",
    "        plt.plot(time, mean, 'b')      \n",
    "        \n",
    "        fig, ax = plt.subplots(figsize=(4, 3))\n",
    "        ax.set_title('Accumulated Hotspot Information Gain Reward')                             \n",
    "        plt.plot(time, hotspot_info, 'r')          \n",
    "        \n",
    "        fig, ax = plt.subplots(figsize=(4, 3))\n",
    "        ax.set_title('Average Regret w.r.t. ' + self.reward_function + ' Reward')                     \n",
    "        plt.plot(time, regret/time, 'b')        \n",
    "        \n",
    "        fig, ax = plt.subplots(figsize=(4, 3))\n",
    "        ax.set_title('Map MSE at 100 Random Test Points')                             \n",
    "        plt.plot(time, MSE, 'r')  \n",
    "   \n",
    "        plt.show()          \n",
    "    \n",
    "                             \n",
    "'''%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n",
    "                                    Aquisition Functions - should have the form:\n",
    "    def alpha(time, xvals, robot_model), where:\n",
    "    * time (int): the current timestep of planning\n",
    "    * xvals (list of float tuples): representing a path i.e. [(3.0, 4.0), (5.6, 7.2), ... ])\n",
    "    * robot_model (GPModel object): the robot's current model of the environment\n",
    "    \n",
    "%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% '''\n",
    "\n",
    "def info_gain(time, xvals, robot_model):\n",
    "    ''' Compute the information gain of a set of potential sample locations with respect to the underlying fucntion\n",
    "        conditioned or previous samples xobs'''        \n",
    "    data = np.array(xvals)\n",
    "    x1 = data[:,0]\n",
    "    x2 = data[:,1]\n",
    "    queries = np.vstack([x1, x2]).T   \n",
    "    xobs = robot_model.xvals\n",
    "\n",
    "    # If the robot hasn't taken any observations yet, simply return the entropy of the potential set\n",
    "    if xobs is None:\n",
    "        Sigma_after = robot_model.kern.K(queries)\n",
    "        entropy_after, sign_after = np.linalg.slogdet(np.eye(Sigma_after.shape[0], Sigma_after.shape[1]) \\\n",
    "                                    + robot_model.variance * Sigma_after)\n",
    "        #print \"Entropy with no obs: \", entropy_after\n",
    "        return 0.5 * sign_after * entropy_after\n",
    "\n",
    "    all_data = np.vstack([xobs, queries])\n",
    "    \n",
    "    # The covariance matrices of the previous observations and combined observations respectively\n",
    "    Sigma_before = robot_model.kern.K(xobs) \n",
    "    Sigma_total = robot_model.kern.K(all_data)       \n",
    "\n",
    "    # The term H(y_a, y_obs)\n",
    "    entropy_before, sign_before =  np.linalg.slogdet(np.eye(Sigma_before.shape[0], Sigma_before.shape[1]) \\\n",
    "                                    + robot_model.variance * Sigma_before)\n",
    "    \n",
    "    # The term H(y_a, y_obs)\n",
    "    entropy_after, sign_after = np.linalg.slogdet(np.eye(Sigma_total.shape[0], Sigma_total.shape[1]) \\\n",
    "                                    + robot_model.variance * Sigma_total)\n",
    "\n",
    "    # The term H(y_a | f)\n",
    "    entropy_total = 2 * np.pi * np.e * sign_after * entropy_after - 2 * np.pi * np.e * sign_before * entropy_before\n",
    "    #print \"Entropy: \", entropy_total\n",
    "\n",
    "\n",
    "    ''' TODO: this term seems like it should still be in the equation, but it makes the IG negative'''\n",
    "    #entropy_const = 0.5 * np.log(2 * np.pi * np.e * robot_model.variance)\n",
    "    entropy_const = 0.0\n",
    "\n",
    "    # This assert should be true, but it's not :(\n",
    "    #assert(entropy_after - entropy_before - entropy_const > 0)\n",
    "    return entropy_total - entropy_const\n",
    "\n",
    "    \n",
    "def mean_UCB(time, xvals, robot_model):\n",
    "    ''' Computes the UCB for a set of points along a trajectory '''\n",
    "    data = np.array(xvals)\n",
    "    x1 = data[:,0]\n",
    "    x2 = data[:,1]\n",
    "    queries = np.vstack([x1, x2]).T   \n",
    "                              \n",
    "    # The GPy interface can predict mean and variance at an array of points; this will be an overestimate\n",
    "    mu, var = robot_model.predict_value(queries)\n",
    "    \n",
    "    delta = 0.9\n",
    "    d = 20\n",
    "    pit = np.pi**2 * (time + 1)**2 / 6.\n",
    "    beta_t = 2 * np.log(d * pit / delta)\n",
    "\n",
    "    return np.sum(mu) + np.sqrt(beta_t) * np.sum(np.fabs(var))\n",
    "\n",
    "def hotspot_info_UCB(time, xvals, robot_model):\n",
    "    ''' The reward information gathered plus the exploitation value gathered'''\n",
    "    data = np.array(xvals)\n",
    "    x1 = data[:,0]\n",
    "    x2 = data[:,1]\n",
    "    queries = np.vstack([x1, x2]).T   \n",
    "                              \n",
    "    LAMBDA = 0.5\n",
    "    mu, var = robot_model.predict_value(queries)\n",
    "    \n",
    "    delta = 0.9\n",
    "    d = 20\n",
    "    pit = np.pi**2 * (time + 1)**2 / 6.\n",
    "    beta_t = 2 * np.log(d * pit / delta)\n",
    "\n",
    "    return info_gain(time, xvals, robot_model) + LAMBDA * np.sum(mu) + np.sqrt(beta_t) * np.sum(np.fabs(var))\n",
    "                              \n",
    "                              \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## The Monte Carlo Search Tree Class\n",
    "For non-myopic horizons we select to use a Monte Carlo tree search in order to select an action plan in a global setting. We create a Monte Calro Search Tree class for convenience."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "class MCTS():\n",
    "    '''Class that establishes a MCTS for nonmyopic planning'''\n",
    "    def __init__(self, computation_budget, belief, initial_pose, planning_limit, frontier_size, path_generator, aquisition_function, time):\n",
    "        '''Initialize with constraints for the planning, including whether there is \n",
    "           a budget or planning horizon\n",
    "           budget - length, time, etc to consider\n",
    "           belief - GP model of the robot current belief state\n",
    "           initial_pose - (x,y,rho) for vehicle'''\n",
    "        self.budget = computation_budget\n",
    "        self.GP = belief\n",
    "        self.cp = initial_pose\n",
    "        self.limit = planning_limit\n",
    "        self.frontier_size = frontier_size\n",
    "        self.path_generator = path_generator\n",
    "        self.spent = 0\n",
    "        self.tree = None\n",
    "        self.aquisition_function = aquisition_function\n",
    "        self.t = time\n",
    "\n",
    "    def get_actions(self):\n",
    "        self.tree = self.initialize_tree()\n",
    "        time_start = time.clock()\n",
    "        \n",
    "        while time.clock() - time_start < self.budget:\n",
    "            current_node = self.tree_policy()\n",
    "            sequence = self.rollout_policy(current_node, self.budget)\n",
    "            reward = self.get_reward(sequence)\n",
    "            self.update_tree(reward, sequence)\n",
    "        best_sequence, cost = self.get_best_child()\n",
    "        return self.tree[best_sequence][0], cost\n",
    "\n",
    "    def initialize_tree(self):\n",
    "        '''Creates a tree instance, which is a dictionary, that keeps track of the nodes in the world'''\n",
    "        tree = {}\n",
    "        #(pose, number of queries)\n",
    "        tree['root'] = (self.cp, 0)\n",
    "        actions = self.path_generator.get_path_set(self.cp)\n",
    "        for action, samples in actions.items():\n",
    "            #(samples, cost, reward, number of times queried)\n",
    "            cost = np.sqrt((self.cp[0]-samples[-1][0])**2 + (self.cp[1]-samples[-1][1])**2)\n",
    "            tree['child '+ str(action)] = (samples, cost, 0, 0)\n",
    "        return tree\n",
    "\n",
    "    def tree_policy(self):\n",
    "        '''Implements the UCB policy to select the child to expand and forward simulate'''\n",
    "        # According to Arora:\n",
    "        #avg_r average reward of all rollouts that have passed through node n\n",
    "        #c_p some constant , 0.1 in literature\n",
    "        #N number of times parent has been evaluated\n",
    "        #n number of time node n has been evaluated\n",
    "        #ucb = avg_r + c_p*np.sqrt(2*np.log(N)/n)\n",
    "        leaf_eval = {}\n",
    "        for i in xrange(self.frontier_size):\n",
    "            node = 'child '+ str(i)\n",
    "            leaf_eval[node] = self.tree[node][2] + 0.1*np.sqrt(2*(np.log(self.tree['root'][1]))/self.tree[node][3])\n",
    "#         print max(leaf_eval, key=leaf_eval.get)\n",
    "        return max(leaf_eval, key=leaf_eval.get)\n",
    "\n",
    "    def rollout_policy(self, node, budget):\n",
    "        '''Select random actions to expand the child node'''\n",
    "        sequence = [node] #include the child node\n",
    "        #TODO use the cost metric to signal action termination, for now using horizon\n",
    "        for i in xrange(self.limit):\n",
    "            actions = self.path_generator.get_path_set(self.tree[node][0][-1]) #plan from the last point in the sample\n",
    "            a = np.random.randint(0,len(actions)) #choose a random path\n",
    "            #TODO add cost metrics\n",
    "#             best_path = actions[a]\n",
    "#             if len(best_path) == 1:\n",
    "#                 best_path = [(best_path[-1][0],best_path[-1][1],best_path[-1][2]-1.14)]\n",
    "#             elif best_path[-1][0] < -9.5 or best_path[-1][0] > 9.5:\n",
    "#                 best_path = (best_path[-1][0],best_path[-1][1],best_path[-1][2]-1.14)\n",
    "#             elif best_path[-1][1] < -9.5 or best_path[-1][0] >9.5:s\n",
    "#                 best_path = (best_path[-1][0],best_path[-1][1],best_path[-1][2]-1.14)\n",
    "#             else:\n",
    "#                 best_path = best_path[-1]\n",
    "            self.tree[node + ' child ' + str(a)] = (actions[a], 0, 0, 0) #add random path to the tree\n",
    "            node = node + ' child ' + str(a)\n",
    "            sequence.append(node)\n",
    "        return sequence #return the sequence of nodes that are made\n",
    "\n",
    "    def get_reward(self, sequence):\n",
    "        '''Evaluate the sequence to get the reward, defined by the percentage of entropy reduction'''\n",
    "        # The process is iterated until the last node of the rollout sequence is reached \n",
    "        # and the total information gain is determined by subtracting the entropies \n",
    "        # of the initial and final belief space.\n",
    "        # reward = infogain / Hinit (joint entropy of current state of the mission)\n",
    "        sim_world = self.GP\n",
    "        samples = []\n",
    "        obs = []\n",
    "        for seq in sequence:\n",
    "            samples.append(self.tree[seq][0])\n",
    "        obs = list(chain.from_iterable(samples))\n",
    "        return self.aquisition_function(time = self.t, xvals = obs, robot_model = sim_world)\n",
    "    \n",
    "    def info_gain(self, xvals, robot_model):\n",
    "        ''' Compute the information gain of a set of potential sample locations with respect to the underlying fucntion\n",
    "            conditioned or previous samples xobs'''        \n",
    "        data = np.array(xvals)\n",
    "        x1 = data[:,0]\n",
    "        x2 = data[:,1]\n",
    "        queries = np.vstack([x1, x2]).T   \n",
    "        xobs = robot_model.xvals\n",
    "\n",
    "        # If the robot hasn't taken any observations yet, simply return the entropy of the potential set\n",
    "        if xobs is None:\n",
    "            Sigma_after = robot_model.kern.K(queries)\n",
    "            entropy_after = 0.5 * np.log(np.linalg.det(np.eye(Sigma_after.shape[0], Sigma_after.shape[1]) \\\n",
    "                                        + robot_model.variance * Sigma_after))\n",
    "            return (0.5*np.log(entropy_after), 0.5*(np.log(entropy_after)))\n",
    "\n",
    "        all_data = np.vstack([xobs, queries])\n",
    "\n",
    "        # The covariance matrices of the previous observations and combined observations respectively\n",
    "        Sigma_before = robot_model.kern.K(xobs) \n",
    "        Sigma_total = robot_model.kern.K(all_data)       \n",
    "\n",
    "        # The term H(y_a, y_obs)\n",
    "        entropy_before = 2 * np.pi * np.e * np.linalg.det(np.eye(Sigma_before.shape[0], Sigma_before.shape[1]) \\\n",
    "                                        + robot_model.variance * Sigma_before)\n",
    "\n",
    "        # The term H(y_a, y_obs)\n",
    "        entropy_after = 2 * np.pi * np.e * np.linalg.det(np.eye(Sigma_total.shape[0], Sigma_total.shape[1]) \\\n",
    "                                        + robot_model.variance * Sigma_total)\n",
    "\n",
    "        # The term H(y_a | f)\n",
    "        entropy_total = 0.5 * np.log(entropy_after / entropy_before)\n",
    "\n",
    "        ''' TODO: this term seems like it should still be in the equation, but it makes the IG negative'''\n",
    "        #entropy_const = 0.5 * np.log(2 * np.pi * np.e * robot_model.variance)\n",
    "        entropy_const = 0.0\n",
    "\n",
    "        # This assert should be true, but it's not :(\n",
    "        #assert(entropy_after - entropy_before - entropy_const > 0)\n",
    "        #return entropy_total - entropsy_const\n",
    "        return (entropy_total, 0.5*np.log(entropy_before))\n",
    "    \n",
    "    def update_tree(self, reward, sequence):\n",
    "        '''Propogate the reward for the sequence'''\n",
    "        #TODO update costs as well\n",
    "        self.tree['root'] = (self.tree['root'][0], self.tree['root'][1]+1)\n",
    "        for seq in sequence:\n",
    "            samples, cost, rew, queries = self.tree[seq]\n",
    "            queries += 1\n",
    "            n = queries\n",
    "            rew = ((n-1)*rew+reward)/n\n",
    "            self.tree[seq] = (samples, cost, rew, queries)\n",
    "\n",
    "    def get_best_child(self):\n",
    "        '''Query the tree for the best child in the actions'''\n",
    "        best = -1000\n",
    "        best_child = None\n",
    "        for i in xrange(self.frontier_size):\n",
    "            r = self.tree['child '+ str(i)][2]\n",
    "            if r > best:\n",
    "                best = r\n",
    "                best_child = 'child '+ str(i)\n",
    "        return best_child, 0"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## The Robot Class\n",
    "This custom Robot class is used to represent a point robot with all of the requisite modeling and planning algortihms necessary to perform informative path planning in unknown Gaussian environments. The Robot class includes:\n",
    "* A ``GPModel`` object, which represents the robot's current knowledge about the world, including prior information, kernel, and previously sampled points\n",
    "* A ``sample_world`` function handel, which it allows it to aquire a noisy sample from the enviroment at a location/set of locations in the environment\n",
    "* A [TODO] object, which represents the set of actions/paths avaliable to a robot from it's current pose\n",
    "* A [TODO], which allows the robot to plan myopic trajectories\n",
    "* A [TODO], which allows the robot to plan nonmyopic trajectories\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "class Robot:\n",
    "    '''The Robot class, which includes the vehicles current model of the world, path set represetnation, and\n",
    "        infromative path planning algorithm.\n",
    "        \n",
    "        * sample_world (method) a function handle that takes a set of locations as input and returns a set of observations\n",
    "        * start_loc (tuple of floats) the location of the robot initially in 2-D space e.g. (0.0, 0.0)\n",
    "        * ranges (tuple of floats): a tuple representing the max/min of 2D rectangular domain i.e. (-10, 10, -50, 50)\n",
    "        * kernel_file (string) a filename specifying the location of the stored kernel values\n",
    "        * kernel_dataset (tuple of nparrays) a tuple (xvals, zvals), where xvals is a Npoint x 2 nparray of type float\n",
    "            and zvals is a Npoint x 1 nparray of type float \n",
    "        * prior_dataset (tuple of nparrays) a tuple (xvals, zvals), where xvals is a Npoint x 2 nparray of type float\n",
    "            and zvals is a Npoint x 1 nparray of type float                \n",
    "        * init_variance (float) the variance parameter of the squared exponential kernel\n",
    "        * init_lengthscale (float) the lengthscale parameter of the squared exponential kernel\n",
    "        * noise (float) the sensor noise parameter of the squared exponential kernel '''\n",
    "    \n",
    "    def __init__(self, sample_world, start_loc = (0.0, 0.0, 0.0), ranges = (-10., 10., -10., 10.), kernel_file = None, \n",
    "            kernel_dataset = None, prior_dataset = None, init_lengthscale = 10.0, init_variance = 100.0, noise = 0.05, \n",
    "            path_generator = 'default', frontier_size = 6, horizon_length = 5, turning_radius = 1, sample_step = 0.5, \n",
    "            evaluation = None, f_rew = 'mean'):\n",
    "        ''' Initialize the robot class with a GP model, initial location, path sets, and prior dataset'''\n",
    "        self.ranges = ranges\n",
    "        self.eval = evaluation\n",
    "        self.loc = start_loc # Initial location of the robot      \n",
    "        self.sample_world = sample_world # A function handel that allows the robot to sample from the environment \n",
    "        \n",
    "        if f_rew == 'hotspot_info':\n",
    "            self.aquisition_function = hotspot_info_UCB\n",
    "        elif f_rew == 'mean':\n",
    "            self.aquisition_function = mean_UCB  \n",
    "        elif f_rew == 'info_gain':\n",
    "            self.aquisition_function = info_gain\n",
    "        else:\n",
    "            raise ValueError('Only \\'hotspot_info\\' and \\'mean\\' and \\'info_gain\\' reward fucntions supported.')\n",
    "\n",
    "        # Initialize the robot's GP model with the initial kernel parameters\n",
    "        self.GP = GPModel(lengthscale = init_lengthscale, variance = init_variance)\n",
    "                \n",
    "        # If both a kernel training dataset and a prior dataset are provided, train the kernel using both\n",
    "        if  kernel_dataset is not None and prior_dataset is not None:\n",
    "            data = np.vstack([prior_dataset[0], kernel_dataset[0]])\n",
    "            observations = np.vstack([prior_dataset[1], kernel_dataset[1]])\n",
    "            self.GP.train_kernel(data, observations, kernel_file) \n",
    "        # Train the kernel using the provided kernel dataset\n",
    "        elif kernel_dataset is not None:\n",
    "            self.GP.train_kernel(kernel_dataset[0], kernel_dataset[1], kernel_file)\n",
    "        # If a kernel file is provided, load the kernel parameters\n",
    "        elif kernel_file is not None:\n",
    "            self.GP.load_kernel()\n",
    "        # No kernel information was provided, so the kernel will be initialized with provided values\n",
    "        else:\n",
    "            pass\n",
    "        \n",
    "        # Incorporate the prior dataset into the model\n",
    "        if prior_dataset is not None:\n",
    "            self.GP.set_data(prior_dataset[0], prior_dataset[1]) \n",
    "        \n",
    "        # The path generation class for the robot\n",
    "        path_options = {'default':Path_Generator(frontier_size, horizon_length, turning_radius, sample_step, ranges),\n",
    "                        'dubins': Dubins_Path_Generator(frontier_size, horizon_length, turning_radius, sample_step, ranges),\n",
    "                        'equal_dubins': Dubins_EqualPath_Generator(frontier_size, horizon_length, turning_radius, sample_step, ranges)}\n",
    "        self.path_generator = path_options[path_generator]\n",
    "        \n",
    "    def visualize_world_model(self):\n",
    "        ''' Visaulize the robots current world model by sampling points uniformly in space and plotting the predicted\n",
    "            function value at those locations. '''\n",
    "        # Generate a set of observations from robot model with which to make contour plots\n",
    "        x1vals = np.linspace(self.ranges[0], self.ranges[1], 100)\n",
    "        x2vals = np.linspace(self.ranges[2], self.ranges[3], 100)\n",
    "        x1, x2 = np.meshgrid(x1vals, x2vals, sparse = False, indexing = 'xy') # dimension: NUM_PTS x NUM_PTS       \n",
    "        data = np.vstack([x1.ravel(), x2.ravel()]).T\n",
    "        observations, var = self.GP.predict_value(data)        \n",
    "        \n",
    "        fig2 = plt.figure(figsize=(4, 3))\n",
    "        ax2 = fig2.add_subplot(111)\n",
    "        ax2.set_xlim(self.ranges[0:2])\n",
    "        ax2.set_ylim(self.ranges[2:])        \n",
    "        ax2.set_title('Countour Plot of the Robot\\'s World Model')     \n",
    "    \n",
    "        plot = ax2.contourf(x1, x2, observations.reshape(x1.shape), cmap = 'viridis')\n",
    "        # Plot the samples taken by the robot\n",
    "        scatter = ax2.scatter(self.GP.xvals[:, 0], self.GP.xvals[:, 1], c = self.GP.zvals.ravel(), s = 10.0, cmap = 'viridis')        \n",
    "        plt.show()           \n",
    "\n",
    "    def choose_trajectory(self, T, t):\n",
    "        ''' Select the best trajectory avaliable to the robot at the current pose, according to the aquisition function.\n",
    "        Input: \n",
    "        * T (int > 0): the length of the planning horization (number of planning iterations) \n",
    "        * t (int > 0): the current planning iteration (value of a point can change with algortihm progress)'''\n",
    "        paths = self.path_generator.get_path_set(self.loc)\n",
    "        value = {}        \n",
    "\n",
    "        for path, points in paths.items():\n",
    "            value[path] =  self.aquisition_function(time = t, xvals = points, robot_model = self.GP)            \n",
    "        try:\n",
    "            return paths[max(value, key = value.get)], value[max(value, key = value.get)], paths\n",
    "        except:\n",
    "            return None\n",
    "    \n",
    "    def collect_observations(self, xobs):\n",
    "        ''' Gather noisy samples of the environment and updates the robot's GP model.\n",
    "        Input: \n",
    "        * xobs (float array): an nparray of floats representing observation locations, with dimension NUM_PTS x 2 '''\n",
    "        zobs = self.sample_world(xobs)       \n",
    "        self.GP.add_data(xobs, zobs)\n",
    "\n",
    "    def myopic_planner(self, T):\n",
    "        ''' Gather noisy samples of the environment and updates the robot's GP model  \n",
    "        Input: \n",
    "        * T (int > 0): the length of the planning horization (number of planning iterations)'''\n",
    "        self.trajectory = []\n",
    "        \n",
    "        for t in xrange(T):\n",
    "            # Select the best trajectory according to the robot's aquisition function\n",
    "            best_path, best_val, all_paths = self.choose_trajectory(T = T, t = t)\n",
    "            \n",
    "            # Given this choice, update the evaluation metrics \n",
    "            self.eval.update_metrics(t, self.GP, all_paths, best_path)            \n",
    "            \n",
    "            if best_path == None:\n",
    "                break\n",
    "            data = np.array(best_path)\n",
    "            x1 = data[:,0]\n",
    "            x2 = data[:,1]\n",
    "            xlocs = np.vstack([x1, x2]).T           \n",
    "            \n",
    "            if len(best_path) != 1:\n",
    "                self.collect_observations(xlocs)\n",
    "                \n",
    "            self.trajectory.append(best_path)\n",
    "            \n",
    "            if len(best_path) == 1:\n",
    "                self.loc = (best_path[-1][0], best_path[-1][1], best_path[-1][2]-0.45)\n",
    "            else:\n",
    "                self.loc = best_path[-1]\n",
    "    \n",
    "    def visualize_trajectory(self):      \n",
    "        ''' Visualize the set of paths chosen by the robot '''\n",
    "        fig, ax = plt.subplots(figsize=(4, 3))\n",
    "        ax.set_xlim(self.ranges[0:2])\n",
    "        ax.set_ylim(self.ranges[2:])\n",
    "        \n",
    "        color = iter(plt.cm.cool(np.linspace(0,1,len(self.trajectory))))\n",
    "        \n",
    "        for i,path in enumerate(self.trajectory):\n",
    "            c = next(color)\n",
    "            f = np.array(path)\n",
    "            plt.plot(f[:,0], f[:,1], c=c, marker='*')\n",
    "        plt.show()\n",
    "    \n",
    "    def plot_information(self):\n",
    "        ''' Visualizes the accumulation of reward and aquisition functions ''' \n",
    "        self.eval.plot_metrics()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "class Nonmyopic_Robot(Robot):\n",
    "    '''This robot inherits from the Robot class, but uses a MCTS in order to perform global horizon planning'''\n",
    "    \n",
    "    def __init__(self, sample_world, start_loc = (0.0, 0.0, 0.0), ranges = (-10., 10., -10., 10.), kernel_file = None, \n",
    "            kernel_dataset = None, prior_dataset = None, init_lengthscale = 10.0, init_variance = 100.0, noise = 0.05, \n",
    "            path_generator = 'default', frontier_size = 6, horizon_length = 5, turning_radius = 1, sample_step = 0.5, \n",
    "            evaluation = None , f_rew = 'mean', computation_budget = 60, rollout_length = 6):\n",
    "        ''' Initialize the robot class with a GP model, initial location, path sets, and prior dataset'''\n",
    "        self.ranges = ranges\n",
    "        self.eval = evaluation\n",
    "        self.loc = start_loc # Initial location of the robot      \n",
    "        self.sample_world = sample_world # A function handel that allows the robot to sample from the environment \n",
    "        self.total_value = {}\n",
    "        self.fs = frontier_size\n",
    "        \n",
    "        if f_rew == 'hotspot_info':\n",
    "            self.aquisition_function = hotspot_info_UCB\n",
    "        elif f_rew == 'mean':\n",
    "            self.aquisition_function = mean_UCB  \n",
    "        elif f_rew == 'info_gain':\n",
    "            self.aquisition_function = info_gain\n",
    "        else:\n",
    "            raise ValueError('Only \\'hotspot_info\\' and \\'mean\\' and \\'info_gain\\' reward fucntions supported.')\n",
    "        \n",
    "        # Initialize the robot's GP model with the initial kernel parameters\n",
    "        self.GP = GPModel(lengthscale = init_lengthscale, variance = init_variance)\n",
    "                \n",
    "        # If both a kernel training dataset and a prior dataset are provided, train the kernel using both\n",
    "        if  kernel_dataset is not None and prior_dataset is not None:\n",
    "            data = np.vstack([prior_dataset[0], kernel_dataset[0]])\n",
    "            observations = np.vstack([prior_dataset[1], kernel_dataset[1]])\n",
    "            self.GP.train_kernel(data, observations, kernel_file) \n",
    "        # Train the kernel using the provided kernel dataset\n",
    "        elif kernel_dataset is not None:\n",
    "            self.GP.train_kernel(kernel_dataset[0], kernel_dataset[1], kernel_file)\n",
    "        # If a kernel file is provided, load the kernel parameters\n",
    "        elif kernel_file is not None:\n",
    "            self.GP.load_kernel()\n",
    "        # No kernel information was provided, so the kernel will be initialized with provided values\n",
    "        else:\n",
    "            pass\n",
    "        \n",
    "        # Incorporate the prior dataset into the model\n",
    "        if prior_dataset is not None:\n",
    "            self.GP.set_data(prior_dataset[0], prior_dataset[1]) \n",
    "        \n",
    "        # The path generation class for the robot\n",
    "        path_options = {'default':Path_Generator(frontier_size, horizon_length, turning_radius, sample_step, ranges),\n",
    "                        'dubins': Dubins_Path_Generator(frontier_size, horizon_length, turning_radius, sample_step, ranges),\n",
    "                        'equal_dubins': Dubins_EqualPath_Generator(frontier_size, horizon_length, turning_radius, sample_step, ranges)}\n",
    "        self.path_generator = path_options[path_generator]\n",
    "        \n",
    "        # Computation limits\n",
    "        self.comp_budget = computation_budget\n",
    "        self.roll_length = rollout_length\n",
    "\n",
    "    def nonmyopic_planner(self, T=3):\n",
    "        ''' Use a monte carlo tree search in order to perform long-horizon planning'''\n",
    "        \n",
    "        self.trajectory = []\n",
    "                 \n",
    "        for t in xrange(T):\n",
    "            #computation_budget, belief, initial_pose, planning_limit, frontier_size, path_generator, aquisition_function, time\n",
    "            mcts = MCTS(self.comp_budget, self.GP, self.loc, self.roll_length, self.fs, self.path_generator, self.aquisition_function, t)\n",
    "            best_path, cost = mcts.get_actions()\n",
    "#             print best_path\n",
    "            data = np.array(best_path)\n",
    "            x1 = data[:,0]\n",
    "            x2 = data[:,1]\n",
    "            xlocs = np.vstack([x1, x2]).T\n",
    "            all_paths = self.path_generator.get_path_set(self.loc)\n",
    "            self.eval.update_metrics(t, self.GP, all_paths, best_path) \n",
    "            self.collect_observations(xlocs)\n",
    "            self.trajectory.append(best_path)                \n",
    "            if len(best_path) == 1:\n",
    "                self.loc = (best_path[-1][0],best_path[-1][1],best_path[-1][2]-1.14)\n",
    "            elif best_path[-1][0] < -9.5 or best_path[-1][0] > 9.5:\n",
    "                self.loc = (best_path[-1][0],best_path[-1][1],best_path[-1][2]-1.14)\n",
    "            elif best_path[-1][1] < -9.5 or best_path[-1][0] >9.5:\n",
    "                self.loc = (best_path[-1][0],best_path[-1][1],best_path[-1][2]-1.14)\n",
    "            else:\n",
    "                self.loc = best_path[-1]\n",
    "        "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Example initialization\n",
    "To use this code, first create a global Gaussian Environment object. Then, create a Robot object, which can optionally be supplied with a prior dataset, a dataset that can be used to train kernel parameters, or a filename from which to load kernel parameters. If the ``visualize`` flag is set in the Environment model, the resulting Gaussian surface will be plotted.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fe6ae564090>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fe6a6003450>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Environment initialized with bounds X1: ( -10.0 , 10.0 )  X2:( -10.0 , 10.0 )\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fe6a5f9ba10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fe6ae564050>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " /home/vpreston/anaconda2/lib/python2.7/site-packages/ipykernel_launcher.py:138: RuntimeWarning:divide by zero encountered in divide\n"
     ]
    },
    {
     "data": {
      "image/png": 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gSgNroRSBJRcT9jI0gy0Ht7AmeQ2r96wmYU8CK39ayfEzx7mk5iVM6zeNni16UqFsBb9DDSuWXEzYOXLqCDM2ziB+dzxrktewdu9ajp85DkD5qPK0qdWGIW2HcEfrO7ix8Y3Ffh8T47HkYsLKmJVjeGzhY6SkplCpXCWi60Rz92V3c3ndy2lXtx2ta7amfFR5v8OMCJZcTNj4bNNnPBT3ELc2vZWRN43kinpXWKvER5ZcTKl3Jv0Mo1eM5tmlz3J53cuZfud0zi13rt9hRTxLLqbUytAMpq6fypOLn2Tr4a30bdmXsd3HWmIJEZZcTKnz8+mfeffbd3lz9ZskJifSplYbZg+aTbfm3fwOzQSw5GJKjc0HNzMxcSLj4sdx+NRhLq11KRN7T2Rw28ER9Wvj0sKSiwl5a/as4fkvnmf6xumUkTL0urgXj1/7OFfWv9K+5BbCLLmYkHPizAm+3PElK3atYOmPS1myfQnnVzifv9/wd+6PuZ96lev5HaLJB0suJiRkaAazNs/i/XXvM3PTTFJSUygjZWhTqw3P3/Q8D135EOdXPN/vME0BWHIxvks6lMQ9M+7hyx1fUv2c6gxtO5S+rfpydYOrI+52BuHEkovxTWp6KmNWjeGJRU9QoWwFxvcaz+C2g+3Xx2HCkospcWfSzzDl2ymMWjaKHw7/QLfm3Xizx5vUr5JbxRpT2lhyMSXidNppEpMTWbJ9Cf+38v/46eefiK4TzayBs+jWvJtd9QlDllxMUJxJP0P87ng+3/Y5n2//nOU7l3My7SQANza+kXd6vUOnpp0sqYQxSy6m2Bw6eYiFWxfyn2//w+Jti0lJTQGgbe22DLtiGNc3up729dvT6HwraBcJilKI/gLgQ6Ax3t3/+6vqYVeK5FWgG5AC3K2qq920YoH/dpMeqaqT3PArgInAOcAc4E9aWu8cHmFOp53mjYQ3mLx2Mgl7EgBoUKUB90bfy01NbuJ3F/6O6udW9zlK44eiFKK/G1ikqi+IyOPA43glQrri1SpqjleudSzQwSWjp4EYvHIkCa4Q/WE3zjC8MrBzgC5AXPEtpgmGuC1xPDz3YZIOJRFTL4aRN43k+guv59qG19rX8U2RCtH3Bm50o00CluAll97AZNfy+FpEqopIXTfuAlU9BOASVBcRWQJUUdUVbvhkoA+WXEKWqjLss2G8veZtWlRvwbzB8+jUtJPfYZkQU5RC9LUzKy6q6h4RqeVGq89vC87Xz2P4rmyGZzd/K0QfAkYtG8Xba97mL9f8hZE3j7Q7u5ls5fs2XVkL0ec2ajbDcis4X6BC9Koao6oxNWvWzCtkU8xUleeWPsdTnz/FkLZDePGWFy2xmBzlK7lkV4ge2OtOd3B/97nhORWiz214g2yGmxDzP1/+D08veZqhlw3l7V5v22Vkk6s8k0tOhejxCs7HuueBxeNnAkPFcxVw1J0+zQM6iUg1EakGdALmudd+FpGr3LyGYoXoQ86Wg1t4bulz9Gvdj4m9J1qLxeSpKIXoXwCmish9wA6gn3ttDt5l6CS8S9H3AKjqIRF5Hljlxnsus3MXeIBfL0XHYZ25IeV02mnum3kfFcpW4NUur1qLxeRLfq4WfUn2/SIAHbMZX4EHc5jWeGB8NsPjgUvzisWUvPSMdAZ+PJBlO5bxbt93qVu5rt8hmVLC6i6YXH22+TOmb5zOy51e5q62d/kdjilFLLmYXE1InEDd8+rycIeH/Q7FlDKWXEyO9h7fy+zNsxnSdghly9jP0EzBWHIx2VJV/vnVP0nXdO6OvtvvcEwpZIcj8xspqSk8OOdBJiZO5O7ou2lVs5XfIZlSyJKL+YWqsnjbYh6e+zDf7/+ev9/wd5658Rm/wzKllCUXA8Dynct5cvGTLNm+hAZVGjB/8HxubXqr32GZUsySS4RLy0jjmSXPMGrZKOqcV4fXurzGH674AxXLVvQ7NFPKWXKJYHt+3sOgTwaxZPsS7mt3H692eZVK5Sv5HZYJE5ZcIlDSoSReWfEKExInADCx90Rio2PzeJcxBWPJJcKMix/H8NnDKRdVjsFtBvPX6/5Ki+ot/A7LhCFLLhHkqx1f8VDcQ3Ru1pnxvcbb74RMUFlyiRDJx5PpN60fjas25v3b36dqxap+h2TCnCWXCLDr2C4GfjyQI6eOMG/wPEsspkRYcgljKakpPLbgMcbGjwXg3b7v0qZ2G5+jMpHCkksYSklN4Z3V7/DK16+w7cg2Hmz/II9c/QgXVbvI79BMBLHkEkaOnznO2FVj+deKf7HvxD6ubnA1b/V8i44X/eaeXsYEnSWXMJCSmsKrX7/Kyyte5uDJg3Rq2omnbniK6xpd53doJoJZcinllu9cTuynsSQdSqJb8248dcNTXNXgKr/DMsaSS2k25dsp3DvzXupXrs+ioYu4ucnNfodkzC/yU1pkvIjsE5F1AcOeEZGfRCTRPboFvPY3EUkSkU0i0jlgeBc3LMnVls4c3kREvhGRLSLyoYhYzYp8mLFxBoOnD+aahteQMCzBEosJOfm5E91EvMLwWb2iqtHuMQdARFoDA4BL3HteF5EoEYkCxuAVqW8NDHTjArzoptUcOAzcV5QFigQb9m9g8PTBtK/XnjmD5lDtnGp+h2TMb+SZXFT1C+BQXuM5vYEPVPW0qm7Dq110pXskqepWVT0DfAD0dkXQbgY+cu+fhFeE3uQgLSONIdOHULFsRT658xPOKXeO3yEZk62i3EN3hIh8606bMg+dBS1CXx04oqppWYabHLz01Usk7ElgbPexNKjSIO83GOOTwiaXsUBTIBrYA7zshgetCD2AiAwTkXgRid+/f3/BIg4D+0/sZ9SyUfRt2Zc7Wt/hdzjG5KpQyUVV96pquqpmAG/hnfZAwYvQHwCqikjZLMNzmu+bqhqjqjE1a9YsTOil2kvLX+Jk2kn+0fEffodiTJ4KlVxEJPC3+n2BzCtJM4EBIlJBRJoAzYGVePWhm7srQ+XxOn1nutKvnwOZh+HAgvYmwIGUA4xZNYaBlw6kZY2WfodjTJ7y/J6LiLwP3AjUEJFdwNPAjSISjXcKsx24H0BV14vIVOB7IA14UFXT3XRGAPOAKGC8qq53s/gr8IGIjATWAO8U29KFkbcS3iIlNYUnrn/C71CMyRfxGg+lT0xMjMbHx/sdRolIy0jjolcvokX1FiwcutDvcEyEE5EEVY3JazyruFgKzNw0k53HdvLQlQ/5HYox+WbJJcSpKi9+9SJNqjahR4sefodjTL7Zb4tC3KJti1j500rGdR9HVJkov8MxJt+s5RLCVJWRX4ykXuV6VgzelDrWcglhc5PmsvTHpbzW5TUqlK3gdzjGFIi1XEJUekY6jy18jGYXNOP+mPv9DseYArOWS4h6fdXrrNu3jmn9plE+yu5CYUofa7mEoB1Hd/C3RX+jc9PO3N7qdr/DMaZQLLmEmCOnjnDbh7cB8EaPN/DuSmFM6WOnRSHkyKkj3PqfW/l277dMv3M6F1a90O+QjCk0a7mEiKOnjtLpP51Ym7yWT+78hO4tuvsdkjFFYi2XEJCankq/af1Yk7yGT/p/Yt/ENWHBkovPVJWH4h5iwdYFvNPrHXpe3NPvkIwpFnZa5LPRK0bzRsIbPH7t49zb7l6/wzGm2Fhy8dGnGz/lLwv+Qr/W/RjVcZTf4RhTrCy5+CR+dzyDPh7ElfWvZFKfSZQR2xQmvNge7YMdR3fQ8/2e1KpUixkDZlh5EBOWrEO3hB07fYwe7/UgJTWFhUMWUvu82n6HZExQWHIpQWkZaQz4aADf7/+euLviuKTWJX6HZEzQWHIpQY8vfJy4pDjGdR/HrU1v9TscY4KqsIXoLxCRBa54/ILMioviec0Vm/9WRC4PeE+sG3+LiMQGDL9CRL5z73lNwvTHNJPXTublFS8zov0Iu4WCiQiFLUT/OLDIFY9f5P4Hr9B8c/cYhleZERG5AK8kSQe8AmpPB5SAHevGzXxfdkXvS7WVP61k2GfDuKnxTYzuPNrvcIwpEYUtRN8br2g8nF08vjcwWT1f41VTrAt0Bhao6iFVPQwsALq416qo6gpXIG0yYVaIXlWJ/TSWupXrMrXfVMpFlfM7JGNKRGH7XGqr6h4AVd0jIrXc8IIWoq/vnmcdHjY2H9zMxgMbGdNtDDXOreF3OMaUmOL+nosVos8iLikOgK7NuvociTElq7DJZW9mvWj3d58bXtBC9Lvc86zDs1UaC9HP2TKHVjVa0aRaE79DMaZEFTa5zMQrGg9nF4+fCQx1V42uAo6606d5QCcRqeY6cjsB89xrP4vIVe4q0VDCqBD9iTMnWPrjUmu1mIhU2EL0LwBTReQ+YAfQz40+B+gGJAEpwD0AqnpIRJ4HVrnxnlPVzE7iB/CuSJ0DxLlHWJj2/TTOpJ+x2yiYiGSF6INEVWkztg1RZaJIvD/R7oVrwkZ+C9HbN3SDJC4pjvX71zO5z2RLLCYi2a+ig2D/if0Mnz2cxlUbM+DSAX6HY4wvrOVSzFLTU+n/UX+Sjyez7J5l9qU5E7EsuRSzR+Y9wpLtS5jcZzLt67f3OxxjfGOnRcVo0dZF/HvVv3n06kcZctkQv8MxxleWXIrRR99/RKVylRh580i/QzHGd5ZciomqMnvLbG5teisVy1b0OxxjfGfJpZh8t+87dh7bSY/mVtDMGLDkUmxmb54NQLfm3XyOxJjQYMmlGKRnpDNx7UQ61O9A3cp1/Q7HmJBgl6KLwYxNM9h8cDMf3vGh36EYEzKs5VJEGZrBC1++QNNqTbm91e1+h2NMyLCWSxGoKo/Oe5RVu1cxofcEospE+R2SMSHDWi5FMHrFaP73m//lTx3+ROxlsXm/wZgIYsmlkN7/7n3+vODP9Gvdj9GdR9svn43JwpJLISzetpjYT2O54cIbmNx3shWRNyYb9qkooLXJa+n7YV9aVG/Bp3d+at/GNSYHllwKYMfRHXSd0pXK5SsTd1cc1c6plvebjIlQdrUon06mnqTPB304kXqCr+79iobnN8z7TcZEMEsu+fRw3MMkJify2cDPuLTWpX6HY0zIK9JpkYhsd0XkE0Uk3g0rtiL1oeLIqSNMWjuJ4e2H071Fd7/DMaZUKI4+l5tUNTrgbuDFWaQ+JMzaPIvUjFQGtx3sdyjGlBrB6NAtliL1QYir0D7e8DH1KtfjyvpX+h2KMaVGUZOLAvNFJEFEhrlhZxWpBwpbpD4k/Hz6Z+YmzeW2lrfZ91mMKYCiduheq6q7RaQWsEBENuYybpGL0bsENgygUaNGBY21wNIy0hg8fTCn007bKZExBVSkQ7Gq7nZ/9wHT8fpMiqtIfXbzK7FC9KrKH2f9kZmbZvJa19fo0KBDUOdnTLgpdHIRkUoiUjnzOV5x+XUUU5H6wsZVHFSVP8//M++seYenbniKEVeO8DMcY0qlopwW1Qamux/slQXeU9W5IrKK4itS74uRX4xk9NejefjKh3n2xmf9DMWYUssK0WfxyopXeGT+I8ReFsv43uOtE9eYLPJbiN4+OQHGrBzDI/Mf4Y7Wd/B2r7ctsRhTBPbpcd5MeJMRcSPofXFv3rvtPcqWsV9GGFMUllyACWsmcP+s++nevDsf3vGhFY83phhEdHJJz0jnhS9f4L6Z99GpaSc+6v8RFcpW8DssY8JCRLb9D508xB8++wPLdy4n+Xgy/Vr3Y2KfiXbjJ2OKUcQll5OpJ+n5fk/id8dz5yV30qNFD/q17mf3wDWmmEVccnlp+Uss37mcaf2mcUfrO/wOx5iwFVF9LhmawYTECdxy0S2WWIwJsohKLst+XMb2I9u5+7K7/Q7FmLAXUcllfOJ4KpevTN9Wff0OxZiwFzHJZVLiJCavncw90fdwbrlz/Q7HmLAXEcll88HN/P6z33PLRbfwUqeX/A7HmIgQEcll8trJZGgGk/tMpnxUeb/DMSYihH1yUVXe++49OjbpSN3Kdf0Ox5iIEfbJ5etdX7PtyDbuanOX36EYE1HCPrlM+W4KFctWtCtExpSwsE4uqempTF0/lV4X96JKhSp+h2NMRAnr5LJw60L2p+xn0KWD/A7FmIgT1sllyndTqFaxGl2bd/U7FGMiTtgmlzPpZ5i9ZTb9Wvezy8/G+CBsfxVdPqqYNE7WAAAExElEQVQ8m0ds5nT6ab9DMSYihUzLRUS6iMgmEUkSkcfzfkfealaqSYMqDYpjUsaYAgqJ5CIiUcAYoCvQGhgoIq39jcoYUxQhkVzwysAmqepWVT0DfAD09jkmY0wRhEpyqQ/sDPh/lxt2FhEZJiLxIhK/f//+EgvOGFNwoZJcsruB7W9KQZZkIXpjTNGESnLZBTQM+L8BsNunWIwxxSAkakWLSFlgM9AR+AmvKP0gVV2fy3v2Az/mY/I1gAPFEWcRWRxnszjOVpriuFBV8zx1CInvuahqmoiMAOYBUcD43BKLe0++zotEJD4/RbODzeKwOCItjpBILgCqOgeY43ccxpjiESp9LsaYMBMJyeVNvwNwLI6zWRxnC7s4QqJD1xgTfiKh5WKM8UHYJpdg/BAyn/NtKCKfi8gGEVkvIn9yw58RkZ9EJNE9upVALNtF5Ds3v3g37AIRWSAiW9zfakGO4eKAZU4UkWMi8l8ltT5EZLyI7BORdQHDsl0H4nnN7TPfisjlQY7jJRHZ6OY1XUSquuGNReRkwLoZF+Q4ctwWIvI3tz42iUjnAs1MVcPugXc5+wfgIqA8sBZoXULzrgtc7p5Xxvv+TmvgGeDPJbwetgM1sgz7J/C4e/448GIJb5dk4MKSWh/ADcDlwLq81gHQDYjD+8b4VcA3QY6jE1DWPX8xII7GgeOVwPrIdlu4/XYtUAFo4j5TUfmdV7i2XHz7IaSq7lHV1e75z8AGsvmdlI96A5Pc80lAnxKcd0fgB1XNz5cfi4WqfgEcyjI4p3XQG5isnq+BqiJSLPVosotDVeerapr792u8b6YHVQ7rIye9gQ9U9bSqbgOS8D5b+RKuySVfP4QMNhFpDLQDvnGDRrgm8Phgn444CswXkQQRGeaG1VbVPeAlQqBWCcSRaQDwfsD/Jb0+MuW0Dvzcb+7FazVlaiIia0RkqYhcXwLzz25bFGl9hGtyydcPIYMagMh5wMfAf6nqMWAs0BSIBvYAL5dAGNeq6uV498l5UERuKIF5ZktEygO9gGlukB/rIy++7Dci8iSQBkxxg/YAjVS1HfAI8J6IBLN8RU7bokjrI1yTi68/hBSRcniJZYqqfgKgqntVNV1VM4C3KEDzsrBUdbf7uw+Y7ua5N7Op7/7uC3YcTldgtarudTGV+PoIkNM6KPH9RkRigR7AXeo6OtxpyEH3PAGvr6NFsGLIZVsUaX2Ea3JZBTQXkSbuiDkAmFkSMxYRAd4BNqjq6IDhgefufYF1Wd9bzHFUEpHKmc/xOg/X4a2HWDdaLDAjmHEEGEjAKVFJr48scloHM4Gh7qrRVcDRzNOnYBCRLsBfgV6qmhIwvKZ4d2dERC4CmgNbgxhHTttiJjBARCqISBMXx8p8TzgYPdKh8MDr+d+Ml/WfLMH5XofXdPwWSHSPbsB/gO/c8JlA3SDHcRFeT/9aYH3mOgCqA4uALe7vBSWwTs4FDgLnBwwrkfWBl9D2AKl4R+L7cloHeKcBY9w+8x0QE+Q4kvD6NDL3k3Fu3NvdNlsLrAZ6BjmOHLcF8KRbH5uArgWZl31D1xgTFOF6WmSM8ZklF2NMUFhyMcYEhSUXY0xQWHIxxgSFJRdjTFBYcjHGBIUlF2NMUPx/0rBvQtk7wEkAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fe6a5df6ad0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fe6a5eca150>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fe6a601fb50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fe6a5fd10d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fe6a5eb3610>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fe6a4b87310>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create a random enviroment sampled from a GP with an RBF kernel and specified hyperparameters, mean function 0 \n",
    "# The enviorment will be constrained by a set of uniformly distributed  sample points of size NUM_PTS x NUM_PTS\n",
    "\n",
    "''' Options include mean, info_gain, and hotspot_info'''\n",
    "reward_function = 'hotspot_info'\n",
    "\n",
    "world = Environment(ranges = (-10., 10., -10., 10.), # x1min, x1max, x2min, x2max constraints\n",
    "                    NUM_PTS = 20, \n",
    "                    variance = 100.0, \n",
    "                    lengthscale = 3.0, \n",
    "                    visualize = True,\n",
    "                    seed = 1)\n",
    "\n",
    "evaluation = Evaluation(world = world, \n",
    "                        reward_function = reward_function)\n",
    "\n",
    "# Gather some prior observations to train the kernel (optional)\n",
    "ranges = (-10, 10, -10, 10)\n",
    "x1observe = np.linspace(ranges[0], ranges[1], 5)\n",
    "x2observe = np.linspace(ranges[2], ranges[3], 5)\n",
    "x1observe, x2observe = np.meshgrid(x1observe, x2observe, sparse = False, indexing = 'xy')  \n",
    "data = np.vstack([x1observe.ravel(), x2observe.ravel()]).T\n",
    "observations = world.sample_value(data)\n",
    "\n",
    "# Create the point robot\n",
    "robot = Robot(sample_world = world.sample_value, \n",
    "              start_loc = (0.0, 0.0, 0.0), \n",
    "              ranges = (-10., 10., -10., 10.),\n",
    "              kernel_file = None,\n",
    "              kernel_dataset = None,\n",
    "              prior_dataset =  None, \n",
    "              init_lengthscale = 3.0, \n",
    "              init_variance = 100.0, \n",
    "              noise = 0.05,\n",
    "              path_generator = 'equal_dubins',\n",
    "              frontier_size = 20, \n",
    "              horizon_length = 5.0, \n",
    "              turning_radius = 0.5, \n",
    "              sample_step = 2.0,\n",
    "              evaluation = evaluation, \n",
    "              f_rew = reward_function)\n",
    "\n",
    "robot.myopic_planner(T = 150)\n",
    "robot.visualize_world_model()\n",
    "robot.visualize_trajectory()\n",
    "robot.plot_information()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "# Implementation Notes\n",
    "* Should thoroughly comment everything at some point\n",
    "* TODO add \"information visualization\" to the system\n",
    "* Change aquisition function interface\n",
    "* Add reward computation interface (UCB accumulation, exploit accumulation, explore accumulation, map entropy, access final map (MSE?) [G]\n",
    "* Check out MCTS (performance guarentees and implementatino) [V]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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SLBH6BKEo+WzbxnXdGYlchKZpuK6L67ocOnSIvr4+otEohw4dIpfLYZom4XCYUChUYQ0/ESiSvEjs6nvKZrMVc/gld9rJgyVCnwCU+5KLH/ZcPu5sNsujjz5KS0sL27ZtK50PkM/nSSaTJBIJJicnGRsbo7e3l2AwWCJ6KBTCMI7vlR5roJhpbi4iJeNb+bFL7rQXHkuEXkS4rksymWR4eJjOzs45E3lsbIzdu3eTz+c555xz8Hq9NSTxeDzEYjFisRiu6xIMBmlqaiKVSpFMJjl69Cj79u3DcRz8fj+hUKhEdK/XOy8izffYmQxw9dxpk5OTtLS0VETAHUtzWcL8sEToRUBRtXYcB8uyGB4eZvny5bOel0gk2LNnD5qmsWnTJnbv3o3X6531vCKBdF0nEokQiURK+0SETCZDMplkcnKSw4cPl1T2ohQPh8MEAoETRqSZ3Gmu67J3716i0WjFYKWUqlHZl6T5wrBE6ONAPReUYRizzm8zmQx79+4lk8mwfv16GhoaSkazueBYRjGlFIFAgEAgQGtra2l7UWVPJpP09vaSTqdRShEMBiuIfqJRDHIpotydZllWxbFLwTHzxxKhF4CZfMnFf5dXwCiHZVns37+fsbEx1q1bR3Nzc4VhaabzqrEQK3e5yl6E4zgllX14eJj9+/eTTqeZmJggHo+XSD5flX0m1AucmU9wDEwPCEvutPpYIvQ8MJsvGeoT03Ec+vr6SrXN1q9fX/fDPpGW63qop7L39PQQCoXQdZ1EIsHg4CDZbBbDMEpz8oWq7HONhINjz81ncqcZhvEHn522ROg5YK6+ZKgktIgwMDDAwYMH6ezsZPv27TWuoCLma4g6UeRXSuH1eonFYhUqu2VZJBIJkskkfX19pFIpgBoru2maM7ZdbrVfaN+AGd1pmUyGXbt2cdpppwF/mO60JUIfA/P1JUPhI3Ich+HhYfbu3UssFmPr1q14PJ5F79uJwEztmqZZV2VPp9MkEgmGh4c5cOAAtm3j8/kqSO7z+UqD0IkwxBVJWvTj67r+B+tOWyJ0HRQ/hnJf8lw/xMnJSdLpNEeOHGHLli2lUrsLwUwf2Yn++Obavq7rhMPhCmOaiJDNZks+83KV3e/3l4o9BoPBRSd3kczFe5iPO63aAPdSdactEboKxaCQPXv20NbWVso5ng2pVIqenh4cx8Hn85XUvhOBxVK5rfExzMbY7AfOA0op/H4/fr+flpaW6WtZFiMjI8Tjcfr7+xekss8Gx3GOScL5GuBeiu60JUJPodyXDJT+PdvLy+Vy7Nu3j3g8zimnnEJTUxOPPPLIC9HlBUNEGPvnLzH+4C8Jbd1K68evR5uaEpwoVd40zVK55k2bNgGFZ15uZS9X2csDY4oq+2xwXXdBUnW27LRyd9rk5CTBYJBAIHBSutP+4Ak9kwtK1/USuevBtm0OHDjA8PAwa9asYePGjYv+UmeyCh/LNTYb3GSC4RuvJ/H7fYgrJB7dQfrPLqft439F8KxtpfZPBKrvR9O0ksre0dFROmYmlb3cX15PZV8ooethJml+9OhROjo6MAyjxp22d+9eUqkUr33taxelDwvBHyyhj+VLhsKcqh5pXNelv7+f/v5+VqxYwfbt21/wudZCVe78/j2MfeXvSQ2OI+70+U4qzcBn/5bw1rPh3YtfZL6IubitjqWyFwNjqlX2ItHL59AnCo7jlLLNiii+ix07diwR+oXGXHzJQI2EFhGOHj3K/v37aW1tZfv27cedCPFCIv3rB5j8t29jeHTCHRFc28WxHJycje2AJi723l0Efvx91EevPyF9OB4JapomjY2NNDY2VrRXVNlHR0cZHx8nl8uRzWYrpPlcVfa5wHGcmvdebDuRSNDQ0LAo11koXjpf5HFiPr5kqPQnj46O0tPTQyQS4ayzzppTvPXxojjQzBRdNR8JbQ/2k/yP7xPuaiU3PA4a6IaO6TNxgy6OGCjXRomLDPYhk6MQnXXVlXljPoElc0G5yg4wMjLC5OQky5YtK/nMjx49SiaTqVDZQ6EQwWBwQdLctu0Zz0skEhWLIr4YeNkTumjBtCxrTkQuQtM00uk0Tz75JLquc9pppxEMBud13RNpKJkroV3XIXHb14ksi6HpGlmmy0eKCBJsREaG0FesRgb7UCLIT34A1yy+lD7Rz6QoPX0+Hz6fb0aV/fDhw6RSKUSEQCBQYWWfLV7gWGp9cZ22FxMvW0Ifjy85k8kwMDBALpfjjDPOIDpPaTWTZJ0rHMeht7eXQ4cOoet6SXUsfnjzaTd313fwexx0XcfOWRW1YNXydTh7ngPA7juA3twCk2NYz+/CmRxHjzbWb3SBON5Isbm0P9M7nkllLwbGjI6O0tvbi2VZeL3eCiu73++vqRhTD0uEPkEoSuS9e/fS2NhILBab04eUz+fZv38/4+PjNDU1oWnavMkM0+r6QmKdBwcH2b9/P52dnWzduhURKX10g4ODJJPJ0ooaSqkS0evN553nn0Ib3IemF+7dyU27X8wNm0k/v7vy+MlJlCg0xyHzf28l9P6/nPe9z3Z/J9KAON9nrmlaSTIXISLkcrmSlb2oshcH1nw+Tzwer6uyLxF6kVHtSy5asmcjc1EiDg4OsmrVKjZs2MDIyAjj4+ML6sd8MqeKKK4XPDY2VgoVLZYaqk6gGBoaYnx8HNM0S1lSjuMQCASmpXkohP7ovWiqrE5ZOlfoX6yF4LI2/CpHIinkdhekNFYeJxiCXJrMM08QSCXQgouXUnmiVW7XdY/bUKmUKqnszc3Npe22bZNMJhkZGalR2UOhEGNjYySTyQUJgP7+fv70T/+UI0eOoGkaH/7wh7n22msZGxvj0ksv5eDBg+zdu/c+4BIROeZH+bIg9EwuKMMwjulLFhEOHz5Mb29vTfJEMSZ7IZgPoTOZDHv27MG2bfx+Pxs3bpxVyiil8Hg8dHR0VPhv0+k0yWSS8fFxnN/+N9F0srQaoTguTjYHuk6key3kM2hOjqgfctu2kti9F3diHC2VhEAQsbJkfnQbwT/56IKeQT2UE9pOpTGCgUVrGwoD84kyWBqGQTQaxePxsHHjRmBaZU8mk9x///3s3LmT888/nzPPPJPvf//782r7pptu4swzzySRSHDWWWfx5je/mVtvvZXzzjuP6667DqXUA8B1wF8fs63juckXG3PxJdcjpYgwPDzMvn37iMVivPKVr6wJOZzJDz0XzKXGtm3b7N+/n9HRUU455RSam5tLi4HPhnpW7mKxgmAwSGtTDH3Xz8hZ04EPjl14Ds7aU/BIhsx4hqLC6M2NY65pI62dQnJkBK+uoyXHST/5KP6L34/mWxziFVVi17LY/d4/BU+YDd/5GkZkcbSAxQwsqQfHcSrU7HKV/ROf+AT33HMPjz32GPF4fF7tlg/M4XCYjRs3cvjwYe6++24eeuih4mG3AQ8xC6FfetHnTBM5m82WVOp6Mbb1CD0xMcHjjz/O0aNH2bJlCxs2bKgbP7wQtbmIY0VyFQNTduzYgd/vZ/v27SXVbj4RYMcivvbkfbjZyiwj2xLMVWtpi3lwDQ96PlN5jmuj+zVijV4a/Dbh1cvxNoTI/Pj2OfVnrn1WStF7/Q1YQ8Pk+/bx7NvexeSvH1uU9k80oW3bPqZKLyIYhkFTU9OCr3Hw4EGeeuoptm3bVopKm2p7EGg99tkvMQk916CQInRdL63gmEql2LNnD67rsnHjxlnL7cwW+nkszDQYjIyM0NPTQ1NTE9u2basboDBXCT0TJJ3EGOkll6yUEq7pJ7qsAc21cTy17jfxBvDGh0kFY5hWFkkliG5cR2pwHwN9Bwk3Nh13hpSIkP+f/yH1+OOlSDUlFvs+/v8Ru/CdrLrhfy+4baiVoIuNY7W/GPaBZDLJRRddxFe/+tUFG9deEoReqC/ZMAyy2Sy7du0ikUiwfv36inzeY+F4JHT1uclkkt27d2MYxjFTKqsJPRPBj0V8/fH/QmkabjZX2iYoAqtXoLvpwm+t9rWrWCsc3k8wNYYbbkRLjOOIRrirnfzBnfQn15TCLavdaHMlkT08TPp7t1aEnQIoDcZ/cjeJx39L62V/TMPrXoW3s31ObZbjhVa562GhpLYsi4suuogrr7yS97znPQC0tbUxODhIR0cHSqkOYGi2dk5qQh+PL9m2bQYHBxkaGmLz5s1s2rRpXg97MSR0Pp+np6eHZDLJhg0bZg0LPN60SBkdQEuN47oO2NMuKmP9ZszE8PRxVcX4ACSVKPRBBPH5ITGOaCZg0ZAZpWHTBcB0HbJyN1p5gEaR5NXTGBEh/tnPgevWELoIe/AQfZ/7Koc9/0RwbSvr7vwh+jys1i+Eyj0ToXO53IKLWIgIH/jAB9i4cSMf//jHS9svvPBCbrvtNq677jqAq4C7Z2vrpCV0kRB79uwp1eCaawpdf38/hw4doqWlhZaWFtrb5z/aH4+EBjh8+DCTk5OsXbt2zoPJXBesq0t8K4fx5M9QCpxcvngg5pr1aMEglBM6naw4VSIxmJjer40OQnMHMjaE29yASo6XFgavV4esPECj3I3m9/tLJE9++zvI6GjhejMQOrw8BAhOzoZMgtF//TatH75m1udRxAuhcs80h47H4wuumvrrX/+a22+/ndNOO40tW7YA8IUvfIHrrruOSy65hFtuuQXgzcDFs7V10hG62pc8MjLChg0bZj1PRDhy5AgHDhygtbWVbdu2kcvl6OnpWVA/FiKhiwkcR44cKSVwzDjnch3IpCCXKkjTluVzNorVEDqbRH/q5yinIHndTAZ0Hc/adRgBP05ysnSoCxCvcmUGIzAxUt1ByOdxlQdd8rjpJCoQoh7Krb3lbrRMJlMg+cMPk//FL1CAqzSow2fDZ6AbhQGt+NQn7v0JjZe/FzM8t/nki6lyH09QyWte85oZB/IHHnig+M/z5tLWSUXo8ooRRYk8F8k2U/JE+cAwX8x3LjQxMcHu3bsJh8N0dnYSi8WOQWYXHvsvGD0MulH4E6Hb8GA7a+Z1XUmMYz73IGTTpbBON2/hW78ezevF1QxIlhHYGwJnbPp8gPFhajA+BO0rcDUDw7VQ+5+FU8+Zc7+KaZDZH/0A987/KPXNEwmSzeRrjvfGgqXzlK4hjotYNkNfu4llN/zdnK75Yqrc8Xj8RY8Sg5OM0FC/esRMiMfj7NmzB8MwOP300wkEKv2lhmHU1I5abJQHhmzevJlQKMS+ffuOLWl3PlwgM4Bjg2MjkRimAuOZn+F2dQNS+K9xGSpQ+aEopQoSvn8X+tEDiG6i8gVrvqvreFd0oYpzWN1bEb/tGpWBF2r5WpS4uEpBqsp/mo4jto3bEICBffMidPapR+m/8Utkhycqr2flao7VTA1DF4ppI7qpYzuF55d89FHGn3+O6PruWck6Wwmi40WxvFQ9JBKJF2ShgtlwUhF6JjJXuwQymQw9PT3kcjnWr18/Y7jd8Ri2ZkO9wJAijjX/lv3PQP/va3eEGlDpCdAU9O6Cpg5UJoEx2o8bbEQ6u1H+wgej55KcYh9BG8miNIXkLTTXRXQDFQyhygxiYtsVhC7X7FTnanRNUPEx8OpItIuUaJg+P5qdBxRoBuJaqORYPU15RuTu+Xca2vyk/Rrp4SR2Jo8g5OLZmmO9kcpC/kor67EIo9/6OnvfczlaIFhhYa/nRnuxVO4lCV0Hxyo0UCz5UkyeqF55Yq7tHS9EpLTc64oVK9i2bVvNRzTTXFgmhmC4t3Z7QwukC5JMiUAwghodRAs24GoKPTWO9PwGNxgD00t48gi4DoigXAdyGUTXoaEJcunKxsvmzwCSKxBKda3FiDbAob3T/c4mCQHSFEMzIsjwITB82K6OIQ5uahIVnD1W2Tm4G3tiAs1QhGI+go1erKxDLpHDSucLxRUccLI5xBVMf60/3gwHsBKFe3F69rHsru8Rfe/7sdrWkkgkKqqWFEnuOM4JNYzNlgu9ROg5wDAMcrkcfX19FckTL0ZRttkCQ4qoZ60WEdj/VMEI1rIMRMHIYUAgEIJ0GfFyKSTagqiCz9i1coCg0nFEKRBQUojuEldQ2TRuczua40DZQFIzfwZITqJWnILhVcjYUaqfYs4XxJ8cKxiwmjqQ8SGUx4+yQR3cBZvvSJo5AAAgAElEQVRnV7utX/60QhVQSuHxGxgeHTtY/sxCGJEQiJAcGIMy67fp0Sh3ruVGxhn9+k2EXrmNZX/xCbSuLqDSjWZZFk899VRpdc5judEWgmNZuZcIXQf1Ki/m83mefPJJurq6jmk1PlEQEVKp1JwCQ4qom9gxdBCSU8ao7JTbqLkD1xtEpUZrG7GzqFQcw3UKpDY8uF4/yrHR7Pw0ER0bt6kNTVxEqcpY3ur5s26itbRgGA7ijaCGj9Zc1glGUVahf1p8BInEENvC8YRgqG9WQksug3Wg1rMggF0VjoqmwMojtkO4LUJ2MoOVLhzjWE5hSj3F8Xw8TXB5K6knHsO+4SPE/vJvMNqXV7jRBgYGOPvss2vcaMVqouVutHA4PG+/8WxW7qKF/8XESUXoIorJE3v37kVE2LRpU8Uc9YXErl27SKVScwoMKULTtIrSr+I6cPDZmuPE4wOxkUAEla4ySOXSTGo+GtwUFgrx+DHdgoHP9fhxBAxxwPCh2VPz0iqDV/n8WUKN0NaFZ+wwjuFFxoZqpLN4/QTtSpVdpSbQfSEsfxg9k2A2p5r7m5/VEpf6vmdPUwxnojDVcC0bT8iDEfCQGUkijos3GiI3Me0zb1jTgi+wrHD8Qz+Gy/5X3T7M5kYbHx+nr6+vVMygnOTHWphvSeVeACYmJtizZw9+v58zzzyT3t7aOed8UJzPzsdY4rouvb29pFIpurq62Lx587xU/Bqj2KHdNXNbibQgrlWYC3uDCApVZXaK6A52uAPlWBjO9AChuQ4ZG0TX8DoFMgsKVa1epyaRWDvS3A66QsvmCoTsWI3mG0TcDuToIVS2MBdVLcvQ4rXRhSqbRG9ohckEpCbhGPNot+eZirDTIuwqV5XSddx0qupkQQPCnY0kjkzgCftLhI6u6cDr1cHjhXwOhg4hjoOaIthsATlKTS+z29bWVjqnuJJHIpEoVakxTbOC5MWKJUsq9zwhIgwNDVUkTxyv66loVJsLoYuBIfv27aOjo4PGxkZaWlrmPV+vWLDOzuPkUtC2hoL+qBBdR0uMFMgMhTlzQxtq4khVSwrH4wd8uLoJWqGgu+04+EYPYThl8eKWEJmS4KIUTrQNGhVKm9JclY6a6MddfgrKzqOJC8rFbW9HlImMDqFnEzPek/L7caNNqCP7kbWvqHuMu39XyZBVsb0O2byxCNZE/TRDPRqmdWUnI0/sRukausekYdVUBtPUe1RKcJ5+GOOs1xeusQAfdHkxg/L6Y+UVS4aGhkpFBtPpNENDQ3VX34zH4wsqbrDYOKkIrZSiu7u7QrotFqFnM4oUA0NCoRBnn302Xq+XeDy+ILdXuZXbOXoAN1sZaumGW7AbOvGM9lOcJEo6Tk6ZeKVMVY+2FiK2AOyChHMNE0EjG1uOaXow4kPomTihgB9X8mSDTYgvSM52iMo0uZxsDtXYgtg5tPQ0cTUEJI+7ch0yPoKqSqsEkGAU5VqoxmZk7AisrX/f8tsHcdJVKnvAD6kMZjiIFvCh6xqarmEla4lfhK8hiOE3adiwgkT/UcIdMXS9QB5JJVEeD8pxcJ599LgIPRO8Xi9er7ciDdKyLJ544gksyyppb8WqowcPHmRycnJGH/VseP/7389PfvITWltb2blzJ0BFtZJVq1Zx//33N85WrQReAvnQi0XomZDJZHjmmWfYt28fmzZtYvPmzaVIs9niua3RkbqqXtHK7WaTuGODNfsdNPKuIhOZjjHXFOALTivdmoGtaudr4gmiAF2Ba+fJBxrItq3DCsXIt65CC0XQDZ2wf3o+LSJIcoK8x0c6b5dCRCvaNT1IOIJdJxNLwoXCepprTRv2qo/JpFDxUazJwuBlNkQIr1tBwyldNK5bRsPqNiJtUYLNYYLtMRrWLsOM1oaSmrEGdHGQdBpvyEPj2g4CscqAITVFXC2fRqbSRE90lJhpmhiGwYoVK9i8eTOvfOUr2bJlC+3t7QwNDXHw4EEuu+wyLrzwwnm3/b73vY+f/exnFdu++MUvct5559HT08N5550HhWols+KkktD1UEyBPJ7z6w0I5YEh69atq1C5iphpMHCzGYa/9gVSzz6N8ngwO7sIbn8dnvZOlKEXSv1MTJAZ3Y8ZqbKIK4VtFyy4ju4hH+3AM1kgvcfNkw/E8KbHkIa2ChdUCfXmippeGAiKUwMRVH56fqqUgdHRhcql8Pj9kKtVde10AhOXVLSVyPhAVftQDLBWwSDu2CAqVmnRlV/fg5PN4W1txhv1YxiFwcixap+9ZhrgujSs6SRxaJjs8LTg8cXCTEWco4eChYQO3QNl78HNFUopKcfC/e2D6K975wmPEquHYlmiP/mTP+G73/0uO3bsWFAfXve613Hw4MGKbeXVSq666io++clPvotZqpXASUjo6vnqYkvouQSGFFFPQid/9QBjt92Mm8+DK0g2R37/XuyhQXLDE4U4bcAX8TPuM4i+4wK8q1eXzs9hUm5ezmsejIYOtIkCqQ03j/jDWHWswqIbiFU7uCmp7KMyTFS2THX3BVCZgp9by9TOk11/FFMKzzjkN5nINdGQLrjS8t4gplO5IqO27wkk9o7KZzW4H62pCdM33b4oheRqDWTFgUdpGuGuVlSkgcy+AxgNEfQyO7pkM4UBMJXBCPimBzgRNNNE8nn04V6Yypc/kS7N2ZJmigUKFys+orxaydT/Z61WAichoauh6/qiEXqugSHV54rrYCcmGPnq58kf2AfUumHcZApPxI84Dh7/9Hw9+dBDFYSujqUGSCsPgYYOcukkYngxglHITNaoxuINQr6K0LpRY0Ev/6Zcf7QQUgqI7kHlaqdh4g+iygJbwg0hXF3QEmM4wSjl1geF4KDI9T6HZ/mGwuLqv30ATVNIvpK8YnqBOnPlMnIopQg3eDE3rEIcl5IqAGDb6NEozsQETt5GM7TpBJRcDpRCknHUnidwOzac8LDPmb6XE7Vi50Jw0hN6Nglt7X8evaUDLTxzPHcqlaKvr2/OgSFFFCV08v98i4kH/xt3qq61iCBO7UvUDQ0XwXUFpQofq5NIkn3uOXxTS6gavgB2HTU+GWxDKQ8KyNs2yhfFdLKoCoNa7eiv6R6oSnjQpqS464/gGh6MqZm5UjN88E7l81WARBqQTBKvzwNOpcvJUC5WcoydTz1J3hHO6n0aaWyCI5WqutS5T2UYdacNvkgAR2k4Y5UDTmkWYVlg+qeNhK6L5vfj5rKYux/DbVnzkq1WUg/l1UoGBwdhDtVK4CQ0is1X5TaWrSL5718n9aPvYR85XLEvn88zPDxMX18fa9eu5YwzzqhLZhEht/NJnInKiC1zZAD5579j7Be/KJEZ6gdJGAEPrl2Ir3YtB3GlNHInH3mk8O8pl1M9ZMRDTiszZDk2eUycYKFkkmg6Ui2dRcCqskorBdkUrjdE0teEnpy+J1Ud5w24pq8QjloFJQ7Oim7E668JJtHERdcU3W0RtvrTGLqGyleS3tV0JFmr3ivvDJbgUBhPNFJyS5VuMZ1G+QrvzEkX8rxL+ywLN5dHclkC+588oSr3sYJKLMta9IULi9VKgOL/Z61WAi8DCa28PkJX/i/i3/wcuScfxmhpx/vWP2bA18jAwADRaJRQKFQ3yktEyD50D9mHf1YIVoCCCmv60JtbCBzcS34yWRFjLFRKZ91j4An7yaeyFZLHtV2UrqHp4KYzZJ58ksBr3jDDTShStoGhRfC55bnJgu0IEm5F2Tmomj8rrx+ylWTUdAM8PlLBFrxuHjXlm7ajnSjXwhBBy5QZxYLRKi1gqv/eADJlDJNwE2JOzWEzCZSVwcwlUE4ete93qFgT7lCVAPF4IVmn3WAYLTlRs115/ShsjKYY9nBlsQUVa0IGDhXuI5PD8HkKpYzERfP5ENvF1/97jIYTt1DcbEElx5M6efnll/PQQw8xMjLC8uXL+bu/+7uKaiUrVqwA+OJc2jrpCD2XUrzV0MJRwu/7OPFvfQF7aBD79q8TjTTQ8fbLGW9qIpOplGIiQvY395P/1b248clCIYDiTscGJ4kzkMb0mXh8jYgUihI4eRsrk8fJO5ghH96gF83QcfIW3rAPAaxUjuxECkQQx8UVhWZopH/7W7yvfVP9e/b4QRS28mJhYFI5gDlWHssfw/GArlw01yaTjBMx/OimUyD71GAiSpEMtuOi8KTHcZWGFetCExeVS2CHGqGxHWNsAC2bLAScVPXHNbyI4S20C6ipZwIFcjNlYDNGBlCBICgNSacq0zRnGITVTO9SFfK/zXAQe2y8wqrN5DgYBth2YfuylXgbo+h+P87EOPbAIXRD0T64C9bNXt1mITiRqZN33nln3e1l1UoA6vsLq3DSEboac52XJEw/fWe9ieWP/KTwAcYnyPyfbxFsbMKvaSR9Adyp2GY3GcctqoMK3Dofn+4xSh+lUhSkrd+DJxKsMOrIFHGnmsIT9OIJ+XE0HTdvFVxIHi/2ZJzcPT9Ae8PbS37dImzNV7IFpbUgUbcy5RGlkXKMQkcEUF5svwfRFPgDIIKppFBZxC2UODbEwRUXq3E5ks9jOEXpLmBlscNNEOsspF96vFPtKlLJJH6fD71alS91dmp+rhmI6Hh9Xqy8U0j7LF5BaXXVbdcwMetoA+LzF+LSKVi+jZZm7CPTiSPKttBizbjDQ3g2nYq3ebpyqxaJwJCJK+AfOog7S2jqQnGiyg8tNk66OfR8UR4YsuY1byR82dUonx+zKYYR9KO5OQiFMVtaUNkk9pHD02QGdJ+vUhpAwXpaT5IoVeMbrmvgFBdf0Isv5MXXEkPSKTwhP7kjQ5iPPYC5/3cVh2fL7Mg5LYgyCr+tA/tIP/w/uBiVpmvALTdUKYWFVki6mOqQoYR8oBGxLXTTBLdq0DJMEBdxLMR1EHEQ18YORMmHa33yULCyKyuHq5sozUSfWjdLUtNqv0Sb0LpPRWutLczoRporiF9CVa0yIxhAqlbrUK6D7xVn4mtuRCG4U+qv0nT0zi4cNJTroj11f92+Hy+OVWT/eAoELjZOOgk9V4k8Y2DI5rMw1mxErCzuIz9By0wU4pbJ4VmzgnzGIrtvX0F6QsGfXAXda9ZVGTWPidhVRJ/BsuradoHtmRT+lgYcpaOlM2QHjxI0FJo45NZuKc2fS/qqppHLaVh3/wf5Z56CcITUjifQL/0ztHXdUwcJHrP21RliI4Cm6bhlhi7NrvIFe/yFwoTVJAds3YPpOOQau/CO91c9GANXKexgM57xQQyxC+aFyTEkGEZr68D0eXFMH97ly7ECAewD+6ZHPa8P6sQIKUMHme6LpkDrWI4c2DN1t2CuXI0RDoEUI1ymn7uKNaMyGcgm4MhBmDgKDW21FzoOvBSqlcBLREJXJDuIVCwls23btpooL80fQI/EMLrPmiLzNDx+k9CrXoO3uRE94C+pyyXMJJ2ZDjksQqBuZpHu91aq5ZaFls9ihv0oQyczMIQ+1A+2XZg/l0d4PfUrkl/+2wKZARJxCEewvn4j1n/cjuSyeAyt9uNy3YIVXNMxylIglW6g8oXfri9MPtKGbfrrklmUwjMlhWzXxYq2V+wjl8KJdqBZ2ZL13FEGVtd6jBUrMXwFK33REGfGYpibz8D1FQoAalL7XEUpNKnti1ezkFBBdVZrN+INB5AyCanKNSURpLMLJ9aG07GK7OG9pdUhFwuzpU6eDIkZcBJK6HooWrrj8fi8AkNYuQmn9/foo4cqNqv4MN4Nm/CMDOLm8zi5PC4m9uhwoUZXnUL0AG4V0ZU/AJl6Re/MupJfmR4M28ZFIzc4hPfw86S7XwcOqGwa7Z7b0Z5+pOY8PTGGqxu4D99PftfTaBddjuQyyPARZOgoDB9FJscwP3QNgWXLKvzKmqZhh1txXEEcG1eZZHQ/MU+2RPTS/fmj6DKtIeXRUcFGjNQ4+CI43gCulcMQQeUzhblypIlgdtpqLlAREGP4PMjGTWQP9OKpzvkGCIbrShVNgd7WhhMME2gpeCjKVsZFORaiNBSCZucxfAFyK7sR04fmOuw9cIB0Oo2u64TDYSKRSClLaiH+4pdC6iSchISe6WE//fTTeL3eeQWGKKVwz3g9PHgHulRGJ2HnwOtD1xS6z4voBoYhSD5HfjxeI6WVzwdVRJcZwgHFncGSOyUxNAQcG7vnebLrzwXbIvRft6CNHiK1bBXq8MHKa6fiaKvX4u7dDWMjZG/7FkZTDMYq3Tv2ww/i/vHl4G8oqN4KrGwKygjm6F5wXCb9zUTtgZI0BXAMH1iVA1HODKG8efD6cWwLpRT6lNvLaV2FkRitOj6Mz6o0iJm6wu3eiJ2zUCNHMUYHS7nfBR9z/efl1V3sdWsLSSFMkXgqb1wBWU8IXz6BEhdBYVhZLNMHms6GdWsxfX4syyrlO4+MjJBOpzEMoyLfeS4kn80otmbN/MovnyicdIQGSoXk8/k8e/fuJR6Ps2HDBpYtWzbvtvRwI32RFayaPFhsHGlehhONoXQT1b8XRgdRjo1qWQZHevG0t2JNxnGTZfNQnw+3vAqJUjjxWkuu5vXUzrOnzidbZjkWgbERvI/cA5NpzMH9AISwSK3thn3PV97HxDCu6SkQLp/HyVvoxWT/KeR2/g6jvQ3z3LdiKEHqlMy1p6Scg0461E4wXtBeBIVTz9UkLvlgM5o1Jc11D/rkUdxwM47hwXCqA0rqf1KuL4TPm4PIatwVK5CxERg5iqYpmEEztsNNSLChtOqHQsh7QnjyhecuZdloluh4rCyW6xSSVRJj4FuGaZrEYrGKNc2KJI/H4wwPD5fyncslebGoQakvL4Ga3HCSEtpxHHp7exkYGCiNfHOVytXQNI1BXwurgjquYWJ7fdMGKAVGrAmiTdC3G2UUspb0rlV4VinSv9uJU1zrt0pi65EITro2Gk/zeZBcrbqt+f11XTnG738LKzagnVIweGlARASr81xUNo3lKGxHFRKQXB0Z6IWBPhgdxVm9Hn1g/3Rjtk3uwH6yXftpWNFZcy3RPRVJBlllYoZa8SSHcP2R6bDK6r4jIILtb8SbHgcR8pEmjDrzcL3OtkIjWkkQa4YOrW24rW3I5CSMVxd2mEIwjIaLy7SxR5kemHq8ujM9YGmug9JAt/OFohC5mYs1zETyeDxeUdSgvHJJPp+fkdDJZHKJ0MfCs88+SyQSKRUFTCaTC07QUIWgatxNr8Lu21m503UgFCvk+K7uhrER3FgrpqeQNRM4/TTSu3bhxBOl5WW0ZV14W5oxQkF8iQTZg33kBwdLRjAxPFCH0PXCRUU30LwKj5vBk6/0z2aj7XizCdChuDJ7NtiM1xvAXr2JhBskk3Ihcjrs/l1JnbcP9OKTe0le/iFCRiVBs6r2dSe0AI3+CI7pr1G3ATTdRPJJcqE2TCuNkRzDbi1EZGlVc2IXhWnXhpG6gO7WeSa6B6d9JVbOxp+unD64uonmMQshqMEYWqoQV2HY01qOaaVxNBNdbDz5JLYRQbcyBUI71rxypE3TpKmpqaKoQT6fL6nriUSCZ599tlSDrCjJfT7fos2hf/azn3HttdfiOA4f/OAHi4vUzQtqnpbAFyStJJ/PV1goDxw4gNfrpbOzVurMBY888giv2r6d/PO/ni77MwVl+tAnpyStFEItdSePPuXqcV2X3MBRCDegr1hVWJHCtjCSYyWfqpvLkRk4inX0CFq2Nl5aBYJoVeqvNDYTf+17iD7/a+h5DiMWqzAmuZpZsI6XWYZFBMsTwiwzQrkeHxgG6axGdmQCq68Xc90paC0t+N/+x+hlkiphhAqhoTUQPJpCz05WRo2JoOsaWW8jecclLBn8I73YXj8KhXekst5b2ogQsGsNX5YniGbWSjfbG0Z0Ezvv4j3wNEaZxLVi7ejRQgCO+MIYI9MuNDH9JYOeFWjAnIpcywRb8EqWTKilEMLbvApPcHH8w0888QSveMUrcBynJMkTiQS33XYb//3f/82b3/xm3va2t/HOd75zQe07jsP69eu57777WL58OVu3buXOO+9k01RSD/Uyc+rgpHRbLXZONBRcTlqkNmBC8hlyRRGoFIahFSK7QjFsXxgn1oGxZSvG2lOml5cxzML8DoWrGdjL1qO/7XLUO/8Utr0BopVrUGuhso8qEER1n0bi9ZeiR6LEzz4fGpsRb2VapeZaZCKVRQSUUuSDTZXH5bOoUISoz6JteZDOV20i0BRC7X6WdM8eXKOQDCGoGcgMmZxLwvUxpjcxnDexpz4LzeMlM0Vmj1ho+Qy2p9CeXkc9t7X6ZXEdb+0C8wCWUZhGGR6N9PLNFdLCCZTF3udSuGV9F+900Ilevr04nZiaHtnVS/scB4rS3uPx0NzczOrVqzn99NO56aab6Ozs5LWvfS0DAwOzNzQDHnvsMdatW8eaNWvweDxcdtll3H33nPIxKnBSqtz1CF0djz1fiAh6tBW3uhCfUmjBCKTGK7ZJIIK26nSyB57FW8dPKoYHa9kG9PbVeIMNpW15w0Ct2wwDvfD7p+FIP1h5VFsnatU6VGsHST2KFih8zJoGyW3vIPw/P0DzVL4O06hTXM+uk/CgTUeaaQrCeg5nw2bs//oBmQ99koCpF5IrZoA3GMayXTTdQAvFiIvgZFMYuHhUMV3RKVimp96NVieEU6/znKCQKVav/q+Uuci8QS+JlvVEhvcUtJCyR6HERSItMD5Y6ktpX5nrzWunwTTwZOPkzWZUrraPC8Wx1lxLp9NcdNFFpdJVC8Hhw4fpmlo8AGD58uXs2LFj3u2clISuxvFK6GJgihZqAN2scOMAIE7JHSKAtKzCXHUaSil2xnVe0RFBTa0mIbqJamjDbF2FZlQWHgw0tWPnc0g6Dp0roXNlofCectCngi5sDPL+CHr5x9HUhGrrRDJJVJnKbqbHmV7CbepZJEZxvAH08lTIybEahSzQ0UR8t432m5+TfdN7MJTUL2mk6Vh2VcUTpQiFQ7hT0w5xXXLZPAE7WSid5DioVGXesgC+eoMNlAocliOPiUnle/A0x8hk2sgYURqqF10omwurTLzggxYXlUsjpg9l59CtDJYnhi4W4rhoKo9j2/NaNH4hsG17wYu9F1Fv6rsQf/lJqXJXY7HKEI2NjTOUqZN0T2GeJ5oOq7Zgrj699DAF8K0+A9W+Dm1ZN56Nr8bTeUoNmYsItXWBOT1Sq8ZmrPD0IgGTnhZ0ffpFuaJIukHGznhrTVualcONVKrvSimccOU2PZMgH6w6TgSzezPStx/Ps/9DUoL1CxzotR+iQpAyd5TPUAQDfopryFn+RiYbVzEaXsaQv5VhfyvDwZWFtZ+rkNKieKgN1EnrtZFVSins9lXo4UDNPnIp3ClVW4mLlCVgiG9apdfMwv3o+UyhWERqZmv3YkBEFiUibfny5fT3T9sJDh06tCCb0UlJ6MWeQyulePbZZ+nr66Np5fq6x7heP2rjazBaV9Td72npwoh1zDpqappGsHMNSVfHKTh8QNNwNIOsEUH3lvlOMUkRRGkauVAb0rUaqZrnup5ad51Wp2qnY1Sp1LkMDW0hZGIMz77fkd+/j6P5JpReVkQByNd5rD6jUmK4yij5fl1fBDfQgB1pRmIdaK0rUK0rcFs6OdS5nYS3coWTrFHfKGVp9acArunHaWirsb4qQEJlg5ZW34VUNCx6rASOC26dGmrzheu6s773461WsnXrVnp6ejhw4AD5fJ4f/OAHC6ogelISuhoLJbRt2/T09DA5OUlbWxuveMUr8MfawKiUSo4niP+Us9EXIe1ORBgdHeX3vQMkNT9Js4G0GSFjRsj6I7hKxxGNpBMgJ160qQ/BcRXxxrVoy1cioWkXiMrUqrHG5AhO1ZxYi1dGbOnpBIau8HRvJKWCLHv2/6JPjnA43UhORQrF/gxfjXRxHAvHKUSEofvJuGGyjomWSyAeP2NmU0kVr7i+xwseL8MdWxiKbsCdmgMofaYCjPUJkNJCaKaGE6lNrhB3eiArr76iyiuuWDnywRZy/mayrnc6IOY4cKwoscWqNmoYBt/4xjd461vfysaNG7nkkkvYvHnz/Ns57p68ADAMY14F70WEI0eOsH//fpYvX05bW1spvU0phR5txZmK73aCTfhWbj7mS6len3ompNNpfv/73+PxeAqBCy2tKKWRTOdQRoyAaZDKaYwlHUQEP1NzVIGU42PIOI1Xtoyi+3yIC/boCObYUaxgA0ZqusqHUpALNRMYn45RN1ITWE3tJReOls9gq2airSEGnxskGsyz4sl/Y9+rP8IoAby6h7BmoekuIIWgEhGy2QxmuInxnA9XFC2+TGHurJuM+9owNQVWrYppu4UPXmmKVNMKcv4Gmo7uIqhq/dIAPq12Xg1TNcdwSGs+Qt4QWplhS1k5nFAjenK8EEvuDaJyKVQ+SwoPTusp9PvWIJqHnK2h47JansfK5TCPw2B1rNTJxVzo/fzzz+f8888/rjZOSkLXq1oyVwkdj8d5/vnnCQaDbN26FY/Hw/PPP18xIGjRVuzRQ0isi0DnDMtAFI+dMqgdq16V4zjs37+fkZERuru7aWxs5He/+x2u6xKJhAgGp+eEkRC0NbocHnMYmjAImRkytofxXABMjWzej7/BQKUS+LqWk2/vxElmkVwTGGZhUXfdwNH8pEKNGONH8CYLoZGWJ1giNICtDHy6gx5rImeN4E2NsPyZu+g760ryjk7fpH8qKnrqueMSNAJksoV7NTSbrKOIWZNMBjtx0PEqu24wQt5RFYY5OxCht2M7zZP7iFGpPWSVH4+qnwBjqinprxQpf4yQlSkUYSjCFyotkSveAI4nQCLUSb8VRvkLbkmvIeRscFD02cvpSk5ieudUBbcuZkudPFkyreAkJXQ16q23XI18Pk9PTw+pVIru7u6KyJ3qMkbKH8ZYcRpGpKleUxUonjvTCx0aGqKnp4dly5ZV1Pg+1qobmqbR1awRDhg83WviikGRDYn7yT0AACAASURBVMNugBVmAgIhXCuHx7RRLU1UX96LBs0BtNVt2JZLPplB0hkoK3biOoVmW9q9jAw04HWPEjn6HM37fkm8+w2IVTlwhrwuyPSFwh4XsWwy3gbyxU+ljsVa6UbFwFC6vu6lL3oGTnwnLTIdJpvRwoSoHaDTKohRpiiJ0hjTQjSVVXDJKZN051lYuq9AZmdK8qoyFbzsXxPEaMwNUN8TPjfMlphxshQ3gJN0Dj0fA0NxpcjHH3+cWCzG1q1ba8LwqiW8UmpOZP7/23vz6LjOKu33d4aaB42WLVmyZVu2LHmIYzsegY+GBGigoQNNSNK3hxsydENzWQxJoFlwQ3+QkACLXgyXQHdCgNtNGvoSAmlwIIHEzmjHSRxHo20NtmbJUpVKNZ7hvX+UzlFVqTRaluWgZy0vy2VV1Vunzn73fvd+9rNhasOMxWK8/PLL9Pb2smvXLqqrq7PC9tlsQoVeib0bZFwqOBTBzrUag6aZLi8pCilfCUKScehxNCWHeIJJXE7fpqpDxlvkw7e6lMSqjIhjvJnEoYCc4UXKTv8R31DrpPW41YwoRjJJ6eB0Qoz0e6vSFJ1kcv6Mv2ZISLJCd3Ab/dJExtbIk1kH0HMTe4AjUITuK0YAIXcZg741nMdDXPWRNMAqcKsOl93BpWWIOPqcBh2xMsw81NvZYqaQe6nwuOEy8dBT4fz587S0tLBixYpph8HP9QyeiVzvbpom7e3t9Pf3s3nz5iyCfyYyB9ZNB49TYu/6dB1ckSUEMniDkBhDNRJEgxX4w13kY/6ZZH9eGTDXbsYcOoespyAeTauTAEU+nVTYi0OLIRspCluPEKotZsw7wUYzzExDECQ1aUIhBHDKZt5w28wzgwsgqSs4nGmWXm+wHjMiUW52o05xenG7ZIw8nWoxZwDhKSMqe5GAoN9LXEtfY1lPYioeJFnBTMWRVC+GCU7FJGXIOGRBQrjpPB9jXX5lpRlxuaiVwBL10FPB8nixWIxXXnmFs2fPsmPHDjZu3DjtGXc2yqFTIdNDDw4O8sILLyDLMvv27ZvSmHOfNxNUVcahSnbbqLzuSmvwLEnFS8wZxJGITDImp5kng+t0kFi3Pf260RDG+EbgVEDbvIfYyo0A+AZPUd35OK5Umh7pUk208UvkUNLG7XWKrM8gmfnPvRr5r31BRpwryRIDBfV0O9bhEJOz5AIJw5giT+LwEnNOCCumtc3TV8PrnvD2Xs/Ez9o4ky2lC8CkLyTP+x64XAQCYYl66Hwht6qqJJNJzp07x9DQEJs2bcrqjJkOiqLMe+CdoijEYjFOnz6NJEns3LlzVmND52LQFiyDPt1xFqfpp1Iew23EiBWvwTnQSlwK4BMTdVWXSJCSPTgzDETVE8grV5DqW4EzMkhcePGPZ5pVReDduIZERQXJjg6k0BD17f8fr9XciN8rkdLBNDSGEz5KfDp+h2aTyyQEZp7zM4wnxPJAMyb7i35PDaFUhDWiE7c0sW7T7ZtCcTF9tk4ZoIwzT4UQONX0etPGLZH2TRPrCAR8jCYAScIjpxhLKRx9+RVUNPx+P8FgkGAwiM/nm7HsNJP80FIy6CXroTONWgiBruscO3YMp9PJ3r17Z23MMH8PbZomkUiElpYWqqqq2LFjx6xnAM/HoAcHB4lGo3g8HtZcsRfdFcClRRCSTKhwXV6FlKSUTTxxamMgKaTWp7103JjYs9XxEpDbp1KwpQapehMaKrVnH0NCR5JgYNRBwG2gyHIWW8xQvEh5yBxTJcQkCTRz8u3ldUlEHUW0qtsY85Tb3HAzz/k5vWgXMdOBYZJF4bTK2EKAQ5o4O6vyeBtpxjm6wCuhqCpy0W527txJRUWFrU330ksvcfz4cVpbW+nr6yMWi+WpzU8vP7Sc5Z4DrDKUruts3bp12jB3KszHoK3zuaqq1NbWUlpaOvOTMjAXg04kEjQ3pxVKvF4vq1evTrOTKrcg2o4i6xp43GgxN7pIomZkiKUc+R4ZgYaKJ+hmuGwrkpa0v2V3MoRuFtpCpYEClXDhBrznGilpf4b28quJmy68jjBuVUcY6c8R1gIIUyIllVIkDWR7UlnN23gxtdcbJ9KgcCq1hoC6gmq5wyaiTLo2it8WRjAlFcY/e/r7THtlVTLQxrPzTjWdiNdNcCjmeJSQXm/viIpSo1NQUJBlhJZeXSQS4cyZM7a4geXFk8nklJns5TP0LJFKpWhoaKC5uZnNmzdTWlo6b3rdXOrYiUSCV199lbNnz3LllVfOawOBifB5Oggh6Ozs5Pjx41RUVLB9+3ZM06S1tZXBwUFMRUUrrsalpT2rWVTEWA4H2mNEJnlIY/zm9q6rytIMkxAkc8Jgh6TBmhqKhk/h7GkGYWAiUEkhKypDyYJ0A4cBUd1JVMmOjEQe0YT055+CWZVj/BHTw0ltMx3JChJqUTalU3EwpmdolmsTrSrpsFu2f574jBNv4BrvVkvpAlkSCFmiN8/8CVVVKS4uZu3atWzbto09e/ZQX19PQUGBrUXW2trKa6+9RkdHB+fPn0cbryAsRMj985//nC1b0uSml156Kev/7rnnHmpqapAkqUWSpMmE/9zPckEruYhobW2luLiY+vr6dJnpAvjcs8lyW+Wv3t5eNm7caEsDzzdcn8lDj46O0tjYSFFREXv37k03XRgGV111FaOjo4TDYbq7u0kmE2wrVBDj/bgJbwnhuERQH0ICFExichCvOcGoMlMauMDhlImuqEJET000mxiCzPmwbjOG4fQRrdzCup7jOJwmslSDQKY/5kMg48yICEZSHpyuQhxamrmmiSl0yad4PN95W1UlBhMBBpMBoJwyd5QyZzhNG835fVl12tRTZTy8FkhgpkB2kjIE49qjE2E5EkGXQcItc+Kci/LiPDOrc+ByuXC5XJSWlpJIJKiqqkJVVUZHRxkeHqazs5NHH32U559/Hrfbjcfj4cCBAzO+bj5s3bqVX/ziF9x2221Zjzc2NvLwww/T0NCA2+1+F/CEJEmbhMijhzyOJWvQ27ZtyzKICzHomYxyeHiYlpYWysrK2Lt3b1YCZD5nYet5Wh45YF3XOX36NOFwmPr6enw+H6Zp2vRSq4HeCvFTqRTtra2Uajq4nDidMOzaQMiowh8boCDZj57zNfrMMEL4kSQoXOUn3FVOYWq8DzyZAPdETVtGMCbc+LxxRlespmqsgd5ek77iOgAcsk4y57L3JwNUOFIEhlrSnV7uAs4XbcLM4MjnNXTTQDMn33JKls3KDCQCDCQCVAajKDnK/Lop2WGlrk+E3Qo6Bk6EAJcDklp2Gc6tmridgo4RFcNITiLqTAfrDO3xePB4PKxcmeaZb9u2jebmZkpLS3nsscfmbdB1dXV5H3/00Ue5/vrrcblcCCHaJUk6DewBnp/qtZasQefiYhh0Mpm0aaFXXHEFXu/ktr2F9NAWq6yqqopNmzZhmqZN7s89Tlh89I6ODtatW0ciaeAhmZ6zZRrgcDFWUMWYWYEaD+OJx3CIdBLLgU5YuPFKCZAkQqvq8J8bQhV6WoK3ILsgmzQd+OQ4wQKFUbOADaOv0eYtYcxdhtDjoGYk3kyT8rEWSiOnxymZAqcWwxfpI+4tYaikFsPhzZvhlvIdtmFi0EDm9ZMEbed9bCpNZB3ZNUPgVhWEYdhhd0oXKNKEGLAqC5JIaAYosolhjp+j0/JyvNqpsmv97O+lqYglDoeDZDLJTTfdxKpVk0f/XCi6u7vZt29f5kNdwLTSt0vWoPO1UCaTM4dK+ZCPHHL27Fm6u7vZuHEjZWVT83yn8rQzIdOgE4kETU1NyLLMrl27cDgc9nryGXM8Hqe5uRmXy8Xu3btxOBz0nU+SjA3iklJ45QRxxudByQqar5izrmK84S7KzbQMTkxT8I47Yo9q0l+2k9X9R/GaY0T1lbgzBAQzz6DBIg9jkQqqzh+jY9VbUElg6DomCsWJHlaOtqCMZ79NRUUerx1LCLyxIapiQ8S8K2gtfnOeq5I/p6Cbkw3aqQhGDRUTJxI55TIp3cAB2PO1JElBlgSmkLJYYW4HRJNWPVrgcQnaBhxzMuiZ6tCzyXJfffXV9PVNVjf9yle+MqUO2RQ5mGkTM0vWoHOhqirRaP7OnZmQScMcGRmxw6Tp2GUWLtRDd3R00N3dzaZNmygtLcUwDLu/NteQrY2mr6+P2tpaioomyBRlRU5OhkupcPThkFJEMxQtJdK91YmSNZweK2VlrC1LTjdAhP4VaxkaWU1pqptYUsky6FyihyfgQMgr2ZA6iTo6lB5FaxpIGTpi6QkZ+a+LNxXClzzPWE5vtJjqXK3n89Dpx7rCPqqCqZzfB3W8Jq0bBggJJBm3CjFtonylm9LEQD0kAi4DuRD6RlROtCvUVRk4Z2EBQogps/apVGpWpcwnnpj7EL1c0QOgEphWuGzJZrnnMyd6OpimycmTJ2lra2P79u0zsssszPcMHYvF6O7uJpVKsXfvXoqLi+0jQz6vPDo6yksvvYRhGOzZsyfLmNPPkfC4nQzoJenaa065yipfKX4vA6X1RJSAHarKksBM6kSqd6DhwExlP9crsjdKBZM4HlAc6IUrEaozy5gBhKLY3OnshSogyVSMNU8iiugi3/UWJPIYtJUNH004kHPE+01BeqIm415sPEckSxNrtAzVzFiDx2HiVCGhyXQNK/zqmIsXW6Rx7z13LOTsrHx43/vex8MPP0wymUSSpHXARuDodM9Zsgadi/meoYUQnD17llgsRllZGTt37sTnm33vzVw3El3XaWpqoquri6KiIjZs2IAQYkqvrOs6LS0ttLa2Ul9fz4YNG6b0BpUlEiE9yJBWiGJme1W3ahJNpe9iWZaRi0tpNyfUV+RkHMXrpmvFLhw52l8uUiRFdoOFbb+SjBZcQSpQmh5YZz2c514WSHbZyaeHKEhOzHg2TQNd5DmHKpCPp57MMPKBscm5Das0pyrKOAFGjCfJxkPw8fN6Ss8wdCGw+CaSLPN68xj/9bskT78y9w07ExeqVvLII49QWVnJ888/z3ve8x7e+c50dWrLli1cd911lpTvIeBj02W44Q1u0KFQiBdffJFEIoHP52PlypVzvvhz8dD9/f28+OKLBAIB6urq0DSNcDhsv07uew8ODnLs2DF8Ph+7du3C7/fne1kbXpdEiV+mN1VCXFcxjGyryl2m7PfSI9LNFwXSaFpnu6ICTfFOEv5PiuwOKJfIGWTn9pMqrMBQnGmBvpyuKwGgZs+xLo+2TMzzEkbeax+LTeajCyGIpSZuzf4xN0rOJqfIEqtWFLKiJIiWjOJRNEoLPJT4VcqCKsUBhRVBCbcjfY5WZYFLFWiGhMthEkmqbFiTDpX/+LLBWDy/t53OC89Gmmg2uPbaa+nq6iKZTNLf38/jjz9u/9/nP/95zpw5gxCiVgjx25lea8meoS9EVyyVStHa2ko8Hmfr1q34/X6GhoZmrTySidl46Hg8TlNTE6qq2kkvXdcpKSmhs7OTaDSK0+mksLCQgoIC3G43bW3pETY7d+6ck/xr7SqD82MKnfFyVolhvBkJaL9TwxQT9VdVgVFHMcZYiir3eQZjJh6fQmxtPa7hMwTkCS8v5UgCuSSNiBnEJ0/IJwvVQaxwNZ7owLikr5yWPJZkYsJLQMtmbXj0CEWJbkY8laRSKXBM9rQ+n49UjkKzMBKYIntzCyc9+B3po4EEFAec9neZLispuF1qZkUOnwtK/GAYwi5TJU0DXZPoGVHx+RRqqlROn9P5/TGDa98y2RymM9poNDrjJrzYWLIGnYvZGLQQgq6uLs6ePcuGDRuyPLJlmDOOoM3BdB7aSmL19PTYzSJW0ktRFNauXWv/biKRIBwO09nZSSgUwuVyUVRUxODgIIWFhfh8vlltNpIE21YbHOtQGUoVUOaI4VbT10WRBWMpB17nxHXyuw16k6txJjUECfD5UDxuOt2b2Wy8jjp+9pZNY1K8puULkWVB3LMCj5Etvuc2UxiSjATEHYXEXQWkXH6Ewwcm+PwBxvIUKbQ8RBOvx0UsR9uvc9jN5tIwiqLikPXxkDq94JlG3mSqrFYU6ISi6d91uhRMxYVT1XnupMn/2iEoDuaw7qa5Z0ZHR5eUuAFcRgY9U+gbDodpamqymVe5X4K1IczVoKfy0OFwmMbGRkpLS9mzZ09aglbX0z26eW4uwzA4d+4cgUCAK664AlmWiUQihMNh2traJnnxgoICHI78wgFBL5QFBB1DbuKGG4ds4JBS+B0pHKqJEOnGCM1QSOgKBgqD3mpWaBMJUkfQx+nETmrir6CioxiJSQbtIv9wg5TkwkMkLXYo+YnLfpIOH6ZXxeVVkDI+vwOBounoZv4oJGVMTpTlmwOGpKAJLyoJoqF+TpwdxTRNfD4fsViMeDyO3++fsXMq4BZ4XenXH40rKIrO5g0uXmtJ8psXDP6Pd2TfH5dTLzQsYYPO9VZTeS9N02htbSUajbJly5Ypd8yFIojouk5raytjY2Ns3boVr9ebxfTKV4pqb2/n/Pnz1NbWZtUsLcNdsyadvEomk4RCIYaHh2lvb8cwDAKBAAUFBZO8+NZKg4GITCIloTgVUngY1jyIVLrNYSTqwBQW79nENHTklasoMmIoCrhkjZCzgFPSlWyMvYLbjCLwZWWuvVKcuPDhlibKRnE8DMklDHirwOnMCtV1TeCWJ28CbinJcNI5iUAiI/JSQQ0zvwGdC/vZs8bAX7revg5jY2M0NzfT399PZ2cnqqraTRXBYBCXyzXpOykL6Eg4EMhUlsk0daisKtF4pcXkz640Wb1iYlOYqXVy2UMvEIQQdHd309nZybp162zO91SYr0FbzxNC0N/fz5kzZ1i7di2bN2/GNM0ps9eQrnm3traycuVKdu/ePaP3cLlcrFy50qYWWu2bU3nxrRWFPHfahyQLXA7rusiE4zKmSDeHJJICTQdJcnCiq4hdawWFShyHYiDrJklHAa3enWyMvUzcdGedmSUgYbpwyDrnKSWiFmGoHpAkdNPEK2cfgfK1UQI4ZQ2HAlpOgJXS4sBkOaK4NtX3KONzq3bVAMDtduN0OqmpqcHtdtvD5EZHR+nt7SWZTOJ2uwkGgxQUFBAIBFjhVyn0mhhCUF4giMYEY3E3/cMxHnvO4Lb3T3xPM4Xcyx76AiGEIBKJ0NTURDAYzBte58OFeGhd13n55ZdxOp3s3r07q9kjnzFrmsapU6dIJBJs3779gmZb5/Pi4XCY4eFhwuF2nOYm4qkyHIoJkkQ4JmOYEpIwiMal9Iif8eXppszp/iBXViVQJIGEDjhJOYKc8u6iJHkOh6QTEx50xU1UeIjLXgZc3knC9nlJIlPYoSqbnOvVqCx3ZrHC/F4fyZwktzA0knp+osaKgInLld4ALNpsW1ubnbOwvhPLeC1CUTKZtIe7j2eMWesLUFSY9uK71vlIaBKvVSkcftXk1DmTjVXpz7cQLLHFxJI16HweT5ZlGhsbiUaj1NfXzyncmUsLpQWrAysWi1FXV0dxcfG0TC8hBAMDA7S1tVFdXc2qVasWpKyRCZfLRVlZmU1XNU2Ttu4RGgcD6IbKWAwiUYGqgNOZ/d7FPoP3X2VwdsALZhSHrGOYaQNJOQKcMepxOHMNV+CVtEm2qkgCIbKjaEWZusRT6k/Q0Oaifr3AMK0xQ5OvjcelEp2CaesiRCKh4Ha7icfjdg5j9+7dtoab5b0tg4eJ2c8rVqywv49IJMLo6KhdhVBVlZJgkL+/pgDUIIwLIy6H3AsIq6dYCEFPTw+RSIRVq1bNGF7nw1yFAkOhEE1NTaxYsQKfz0dhYeG0SS+Lf+10Otm1a9cFDy+bLQzDIBXpoEpKUruljlc6fbzWqaDpEpIkcDjS1ymghNldPsDYWAGrS3x09cdxyjqJjDDY5ZysVSBJEilDxpUzPF6WJz8uSxLxlILHOfk6V5Ukeb4V1LMSG6vSnOt8Ge7pTiWqNkRj43nGxsYwDIOKigqKiopsD219L5YBWmG5dWTKzIV4vV58Ph8VFRV2QtMK1UdHu2k/lbIjK5fLlddTj46OXpSmjAvBkjZowA6v/X6/3VY4H68325DbCpej0Sjbtm3D4/EwMDDAiRMnKCwspLCwkEAgYN88lpSNVbqaryDCfDA4OMjp06ezooGDdQYH6wwaz0m82KqiGQrrVmjsqY4TDqe55WNjY3iLqykOOLJq84oMuj7ZqHQh48rTKaXpEq6cOyiuq3kNWpGh0G/S3qviVEzWVOSnfJp5GjUAAm6T9WvLaWoKUVpaSmVlJWNjY/T29tLS0oIkSXaoHQwG8Xq9WRrpE68/4cUz/7aeX1RUZEdf8Xic9vZ2otEor7zyCoCtR+ZyuYhEImzcuHHqL2gWuP322/n1r3+N0+lkw4YN/PCHP6SwMD2e+J577uGBBx5AURRaW1vfKYR4fIaXQ5ojH/Xikldz0NDQwPnz59m8eTMFBQU0NDRQWVk5r3PLuXPnEELYZ9FcZI7Pqa6upry83P6yIc1oCoVChMNhIpGI3R8bCqVvsJqamllxwxcCFnHGMAw2b948LTElHM1W37QQiyfoPx8jnPIhMnS1x5IqLkcuA03gd+aZIJmEQm9240Q0JijzT850d4+4OdZZQiicNvZt60xKSxR0U8o6V0tIRBKT3XSFP4I2eIyamhpbfCITVjIsHA4TDoeJxWJ2MqywsJBgMDhlGdAK0a3v2/ojSRJnz57F5/NRVlaGEIKxsTHC4TC/+c1v+Na3vsWKFSv48z//c/7lX/5lXt//7373O972trehqip33nknAPfeey+NjY3ccMMNHD16lJ6eHtavX98GTCtuAEvcQ69atYoNGzbYHuRCe6Knar+0ZlJZ7Yr5kl5+vx+/309lZSWGYXDq1CmGh4cpKioiEolw7NgxAoGAnYGeLVFkrujv76etrY3169fb2fDpkM+YAbweNw6XhNMwsnjcDsUkN7slSemGiNz5cl5XmkrpyDg759MGMwV0hAvt8B9gMAQvt4wnsXyCVSUSxUFQ3fmNLhU5x65pWHWKolBUVJTV1GKRec6fP09bW5tdBrSM3FL8tP7Y6x0Pzfv6+jh//jxlZWX2/WCF6rfeeiuvv/46H/7wh1FVdd6b+Tve8Q7753379vHf//3fQLa4wbp16wBmFDeAJW7QwWBwQVVLcp9rtTf29fXZM6mmS3pBWjzQGn1TW1tr/06+EpPb7bZryAUFBRfkwS0xBkVRFuyMXlHiJBKPZcVdTsUkYShZI2kkSSKWEPg9uddDIhxTKQ1MeG8lz1TJjiEvOi4cmeftjP8fjUqMRqHYbzIajbN/p4eoNnGtJEz2bK9GVea2Qbrdbtxud1YZ0PKwnZ2djI2NoaqqXUkIBoO43emJnK2traRSKbsfPTdEHxoa4g9/+AM333wzb35zvt7vuePBBx/kwx/+MDA/cQNY4gZ9IUPrcpGbFLP6oi3ZIWDapFcqlaKlpQXTNPPK+eYrMSUSCUKhEAMDA5w+fRrA9g4Wp3smL56pXDJVuDlfyLJEwOsgHpk4R0sSGIZky+FaSOiOvPOoUnr2tXI6ss/auinRE017TVWd+KyR2ITgnwWPS9B7Hp56Ic6+K8BQ0zzpEr+YszHngyzLNuGkqqoqvf5Uyg7Vu7q6iMfjpFIpioqKWLt27aRzuKIovPDCC3zyk5/kvvvum5Xs0GzEDb7yla+gqip//dd/DcxP3ACWuEHnYiFkiDRNo6WlhUQiYSe9rChgqlJUb28vnZ2dbNiwYVp1k1y43W5WrVplZ0INw7DPeL29vXYXmOXFM5NtMKF0kqlcstCoKHbQNzqh/AH5w+7McBnS/codA066R5ykNKguSx9nVEUiril4HOnNs33IjzF+mymKhCyDSzEZjuRLiKX/1k145hV4864UuuxkReDC2hung6XhVlJSYn/PW7ZsQdd1+vr6aG1ttRNmhw8fpquri+eee45f/OIXbNgw/eRSCzOJG/zoRz/iscce48knn7Tvv/mIG8BlaNDzlSGyuNNHjx5l3bp1rFq1akamVzQazRpNO1ceeC4URUnPjR7PhAsh7GRbV1eXnWwrKChA13VGRkaora2d01CBuUKWJWrL4cU2J8I0CHoMHLJB0lCzZlA5VEhqEi6HIBSVae11k9RVJAm6Q36SSZ3aqrQRJ8YNOqHJ9EQLsvYGVZUo9MFoHvGZUESHjLE6R46n2FUvLqpBQzoya25uRpIk9uzZYx+NKirSA/YMw6Cjo4NHH32U3t5enE4nJ06cmLVBT4dDhw5x77338vTTT2dp2r3vfe/jxhtv5FOf+hQ9PT0wC3EDWOJZbsMwsjzy0NCQzYmeC2KxGCdPniSRSLB//34URZnWK1uEkoGBAWpra+0ywmJgdHSUhoaG9GD68ahiKj73QiKagN+ddDASVXAqBi7FwOMCn1sgSeM1ak1nYNRBb8iBJGXQIzWNYNDB2sIQ5cU60ZhJmT9B61ARvWPZ1MiBQQ05OcxwPGdCqKQTT4hJZJOAF/75ry9e9cCSU167di3l5eV5f+fZZ5/l05/+NF/60pf4y7/8S1tyeSGqGjU1NSSTSXvT3rdvH/fffz+QDsMffPBBVFWltbX13bPph76sDDoUCtHd3c2WLVtm9XyrMWJgYID169fb4vlTNVJY72FJ+maeoS42MuvZmXpiViInFAoRCoWIRqO4XC67Jh4MBi84ctA0jebmZjRToSG8A81U0DRBMpXZPy5wyEZWictCMpGiuMiFECabV43ikDS8qsbLfavIbeEaCelo8RShsexr71UjjEQnU2R3bZL4q/+18N+Bdb37+vrYsmVLXhUbwzD45je/ye9//3t+8pOfUF1dveDrmANmtYsv6ZD7QkQOhoeHaW5uZtWqVezZsydNkWxr46WXXrKNoaCgwM4WW3rZFqEkn6TvxUI0GqWpqYmCggKuuuqqSbrgViInM9kWDRN0IwAAGU9JREFUDodtYokQwk7IFRYWzirZZsF6DasMtlVP8bPnXOCQSaYy92+JpK6QL7kuYSXUZFr6gqwvDdMz6iOfII7bCYODk9dWGPAwkicML/EOE4m48Pv9CxaZaJpmideza9euvJ52YGCA2267ja1bt/Lkk08uGvPvQrGkPbRpmlkSuolEgoaGBnbt2jXlcyzSRSKRoK6uDrfbnRVe67pue7twOIyu6zgcDqLRKFVVVYvqlS2BBGvW9HyJ/hapwvpciUQCr9ebl9lmwWoD1TSNurq6rBvWMOG/X3ASiskkk9kqL+lgIPeIIvB7JyIeSRhIqoQsTb6Opm5wun3yFMuVBTpnB7IfkyTB//m2PhLREaLRKA6Hw964MjfjucCi9E6X4Dxy5Ai33347X/7yl/mLv/iLi3LEmQdmtYjLyqB1Xef48eN2mSlrYePZ6Pb2dtvbTNenDBND4kzTtOcYxePxGY1hITA2NkZTUxPFxcWsW7duQd/DSraFw2FCoRCjo6MoimJHJUII2tra7HNjvmsjBPzyqINzQ7laaAJVnbxWGQOPZyLgU2QzS+jAgmmanD4zObHpkHXGcghma1fCP7xvwnumUil7I7Y2Y7/fb3+u6by4EIKOjg6GhobYunVr3g44wzD42te+xtNPP81PfvKTKVmFlwhvvJB7Kj52NBqlsbHRzkZn/t5Upaiuri5baD8zizxV5jkzTL+Q8pFFZhkaGqKuru6idOtIkoTP57ObDyAdZo6MjNDe3k4sFsPlcjE8PGxvZrnGIElw7V6N376scqpXySDQ5H/PZNIk00Y0DRzOyRpuabHEbIVfr9tkJDz5NWursp/rdDondZpZRJGOjo4pvXgymaShoYFAIMCuXbvybp79/f3ccsst7Ny5kyeeeOKilAgXA0vaoHORLxvd1tbG4OAgdXV1FBQUzMj0ikQiNDc3U1hYOOm8ar2HZQyrV6eJOZZnsJRETNO0CSJzObNao3FXrFgxK8GDhUQ0GqWtrY3Vq1dTWVmZxUu2GjZcLlcWs01VVf58p070WegNW7dK/iDNzIn0ZEXm/GCM0rLJuQinQ8o6nwe95DXoTVXTX9OpiCLW0aOzs5NEIoGmaZSXl0/Zzvr0009z5513cs899/Dud797qYTY88JlZdCZsJJe5eXl7NmzB5ie6WUYBu3t7YyMjLB58+Y5ecZcz5B5Zm1ubrYJIpaB52pbWY34oVCI+vr6RVWKtDa9UCjE9u3b7WSfRZbINAYr2TY0NGQLAQSDQfauLeSxk1XoQrVbWidJROW55g5H/rJOadCge2ji99U8It9+D1TMo/xufVelpaWcOXMGSZKoq6sjFovZdE+Hw4HH4+HEiRM0NjZy/PhxfvOb31BZWTn3N1xiWNIGnW+ntCZgpFIpduzYgcvlmramDGnjb21tpaKiwm6GvxDkNgIIIYhGo4RCIc6ePWvfNIWFhaiqSk9Pz4K991wQiURobGxk5cqV7Nq1a8b3zuU+Z3Yw1fibaApvQZJlTF23J1dYcDiUcbnciffwBVyEhuMUFmefV8+0h/GrBs7CEmJJiVhyskFvqsz/Xc4G8XichoYGSkpK2LlzJ5IkUVJSkuXFT548yYMPPkhPTw+FhYV0dnYuG/RiIFfkIBaL2RK9MzG9UqkUp06dQtO0vPzrhVxjZjcWpMksLS0tRCIRnE4nfX19xOPxrDD9YiHznL5ly5Z5RwSZG1d1NSSeT9Ix4iEyplNYlG3QkiSRSOj4fOke63AowWgoyehokpKIxooyD25P+jn+gIvjz5+jsGCUq/ZV0T8y+burrZrXkhkYGODMmTPU1dXlJQQJIXjmmWf453/+Z+677z7e+c53YhjGBY1ZWkpY8gYNExnhTN7zTEkvq6Fh/fr1lJWVLapntCKC1atXs2PHDptZZGWde3p6SCaTdobWCtMXYo1WgrC4uHjBz+nv3Sfx/d+mJrVRAmiawUBvlHhMY2w0maU4Gk/KdHUncToEBQWqna4NhTXamnvoPy8IBh0E/Sper4rX56Bm9ezHFQF2S2sikZiS967rOnfffTfHjh3j0KFDdsJQVdULJucsFSzpshWkp9gPDAxQX19PMBikoaGBeDxue45gMJh108ZiMZqbm/F4PNTU1CxqtlLXdU6dOkU8Hqeurm5acUArKWUlcKykVGY2fS7UQmuGV19fH3V1dRdNjXIkIvh/n1JQXZbmlkloMMyp5mFkVcGRpzbscCooGcRwYQp62nsRQlBX4+f02ey69J7tXj7zkdk3wUSjURoaGli1ahVVVVV5N8aenh5uueUW3vzmN/PFL37xohjwTTfdxGOPPUZZWRmvv/46kN7cP/zhD9PR0UF1dTU/+9nPJg0inCUu/zo0pEMoK2S0zsrJZJKRkRG7xmo1NKRSKSKRCJs3b15U/jWkeeanTp2atrY7E6x2S6vWKkmSHZEUFhZO2dxvCeYFg8Fph90tFJ5rEBxrczLYP8a59mH0DH1eX9CbO3QyzQX3ZBv6cN8wCgk03WFPmrRwx81l7N42O6ae1SFlbfi5EELwxBNP8IUvfIFvfOMbXHPNNbP7kPPA4cOH8fv9/O3f/q1t0HfccQfFxcV89rOf5atf/SojIyPce++983n5N4ZBnzp1yi43THVWHhoaoqWlBYdjQiPLMoSioqKLStuzhP4txtVc5lTNBF3X7TA9FAqhaVpWmO71eunp6aGrq2vRN7F/+d55WtoTkx4PBF32ZEgLwjTx+LNzBvFoghJXiPae7N8N+CS+d9dqnDMMbrY6pAA2b96c1+NqmsaXv/xlXn31VX784x9P2XyxkOjo6OC9732vbdC1tbU89dRTlJeX09vby1vf+lZaWlrm89KXP7EkmUzy8Y9/nK6uLurr6zlw4ABvetOb2Lx5M7Iso2kabW1tjI2NsWPHDptgn2kIXV1daJqWVTf2eDwLcl61EjDr1q2b12TLmaCqKiUlJTbxJbNR49SpU4yMjOBwOOx6+UwznhYKQgjetXeMlnaF3PssFtNweWY+Kri9LkKjLsgRTdi6QeOVV45PG51EIhEaGhpYs2bNlNFQV1cXN998M29/+9s5dOjQoum95aK/v9/eSMrLyxkYGJjhGReGJW3QLpeLQ4cOoes6r732Gk8//TT33HMPzc3NBAIBRkdH+cY3vsH+/fuzvHA+Q7DqxtZUSp/PR1FR0bwSUqlUyvYOiynZK8sygUCAaDRqi/j7fD5GRkbo6emxJYryNZ8sFBKJBI2NjRT6/WzbVMzJ1hwaZ55ssSRLkzYbSZJIGCq5Bv2Bd61h7eoaNE2zv7Ouri5SqRR+v98uEW7bti1v9l4IweOPP85dd93Fv/7rv/K2t71tQT735YIlH3Lnww9+8AN+9atf8Wd/9me8+uqrnDhxgtLSUg4cOMCBAwe46qqrph3qbt0U1jl8bGwMt9tth+i5ibbM5/X399Pe3j5n9ZKFQCqVssfWbtq0KW/CT9O0Sc0nCxWdWBFJbW0txcXFjI7pfOx/95M7W87jdSIp2b5CksHlzt5cUokUIwMTFLHq1U7uu6Niys9+8uRJTNPE7XZn0TwzyTxf+tKXaGpq4kc/+tGsRBQXGpc65L4sDTq3udyac3X48GEOHz7MsWPHcDqd7Nu3jwMHDrB//35bb3kqxONxQqEQIyMjdqLNMgKrTNbU1ITD4ZjSmC4mLGOa60aSGZ2EQqF5NZ/ouk5LSwuGYVBXV5f12e9/eIQjL+fOsxF4g9kbqmkaeP2Ts/5ufYSRsMFoVPB31xbxnrdO7jizJotaRxsL1ligUCjEvffey1NPPUVlZSWf+cxnbLG9xUauQd9+++2UlJTYSbHh4WHuu++++bz0G9egZ4IQguHhYZ599lkOHz7Mc889RzKZZPfu3ezfv583velNM2aiMznBAwMDtqpEeXn5tBnnhYalgWaaJps3b77gEDqz+SQUCs3YfGIZ01TnVU0z+X/+Y4ATrUkSWrqJw9QN/EXZ1FqHbKC4sw1aMlO8aYtg9QoFn09l8wYPbtfE5iKEoLOzk8HBwSk7pIQQ/M///A9f/vKX+frXv04gEKC7u5u/+qu/uqDrNB/ccMMNPPXUUwwNDbFy5Upb4eS6667j7NmzrFmzhp///OfzHcbwp2vQ+TA2NsaLL77I4cOHOXLkCENDQ2zfvt0O02tqaiZ5qng8TlNTEx6Ph/Xr12eF6Zqm2TrcVsZ5oZNi58+fp7W19aIl3Sxkbl7hcBjDMAgGg2iaRjweZ9u2bdMeYSz0DaZ45HcjvNKcQDj86Th7HKZh2JluvyPFe9/i5k1XTl2aSqVSvP766wQCgSlLcclkki9+8YucOXOGhx56aNGPQIuMZYOeDqlUipdffpnDhw/zzDPP2GfD/fv3s2/fPl599VXq6+vt82IuLB1uyxBisRher9dOtAUCgXkboEVQyRRpWExEo1FOnjyJw+FAluUZm0/yQQhBV1+KxjNJ2s+l6B0yKCp0cO3b/aytmD7KGB4epqWlhY0bN1JaWpr3dzo7O/nIRz7C+973Pm6//fZFyWJXV1cTCARQFAVVVXnppZcu+ntmYNmg5wLDMGhsbOQXv/gF3/ve91i1ahXl5eX2OXzXrl3TGpYVymYm2qxZzlaibTY33cjICC0tLVRVVdmD1BYLFmW2s7Mzq66d2XxifTar+SSz1fJCYXWGhcNhtmzZkvd6CyH49a9/zd133813v/vdBRO5nw2qq6t56aWXptxkLjKWDXo+OHbsGLqus2/fPtrb220Pfvz4cfx+P/v37+fgwYPs3bt3Ri+cy/ySZTkr0ZZ5VjUMgzNnzhCJRKivr5/3TOn5QtM0mpqaUBSF2traGQ00mUxmfTZL12y+zSeJRILXX3/dVnDJd12TySSf//znOXv2LA899NCiG9ayQb+BYM1+PnLkCIcPH+bFF1/ENE327t1rn8Mz5w/nQ2ZJKRQK2UIJbrebvr4+Vq9ePSUX+WLCaibJzSLPBZnNJ6FQaE7NJ5ZQoTWOKB/a29u5+eab+cAHPsCnP/3pRRWHsLBu3Tq7WnLbbbdx6623LubbLxv0xYQQgtHRUZ5//nmefvppnn32WUZHR7nyyis5cOAABw8eZO3atTMaeEtLCyMjI7jdbnRdtxNtRUVFFyXRlgnTNDlz5gyjo6NThrjzxWyaTyRJsptZ6uvr82bwhRD88pe/5L777uN73/verEbPXCxYfe0DAwNcc801fPvb3+Ytb3nLYr39skEvNhKJBEePHrXDdIuyevDgQQ4ePGhTViGddW9sbGTFihW20qgQwk60jYyM2Im2iyFYaHUoWfrjixEVZB5BRkZGiMfjtmJKvlJgIpHgc5/7HP39/Tz44IOLOnt7Jtx11134/X4+85nPLNZbLhv0pYZFWbVKZS0tLaxduxan00llZSWf/exnp22lE0IQj8ftRFskErGTUUVFRfOaaGmRcLq7uy9qm+V0sDqkNm3ahBBiUvNJd3c3uq7zta99jRtuuIFPfOITlyTEzkQ0GsU0TZt6e8011/DFL36Rd73rXYu1hGWDXmowDIO3ve1t9tny9ddfp6SkxA7Rr7rqqhkF/q1klMVoA7ISbdMRT1KpFI2NjbjdbjZu3LjoDQuGYdiyyXV1dZMSb1Yp8Gtf+xq//OUvkWWZgwcP8qMf/WhR15kPbW1tXHvttUB6o77xxhv5/Oc/v5hLWDbopYjz58/bTSOZlNUjR45w7NgxHA4H+/bt4+DBg+zfv5/CwsJpw+HMwQGhUMjmblv1cCtbbvVrL/RI2tlibGyMhoYGKisrpyzHxeNx7rzzToaHh3nggQcoLCxkcHDwjU4YmS2WDfpygxCCkZERnnnmmXlTVi3uthWmJxIJW3fNyiIvdm3bCvGn0zdraWnh1ltv5W/+5m/4p3/6p0seYi9BXL4G/fOf/5y77rqLpqYmjh49yu7du+3/u+eee3jggQdQFIVvfetbvPOd71yMJV0yRKNRXnjhBTvRNjg4OCNl1YLVN1xSUoLL5bKH3bndbtuDT9VZthCwatuqqlJbW5s3xBdC8F//9V9861vf4t///d+zvuuLiUOHDvGJT3wCwzC4+eab+exnP7so73sBuHwNuqmpCVmWue222/j6179uf8mNjY3ccMMNHD16lJ6eHq6++mpaW1svWfP6pYCmaRw/ftw28La2NjZt2mQTXrZu3YokSbS2ttoklUyvKIQgkUhMknDKTLQtBOvLauqorq62B97nIhaLcfvttxONRvnBD36waIorhmGwadMmfv/731NZWclVV13FT3/6U+rr6xfl/eeJy1expK6uLu/jjz76KNdffz0ul4t169ZRU1PD0aNH2b9//yKv8NLBOmPv27ePO+64A9M0aWxs5Omnn+bb3/42r776KolEgre//e1cd911k4xTkiQ8Hg8ej8dWvbSaMyyBfWDeEk6WWOHAwECWsH8umpqauO2227jpppv4h3/4h0UNsY8ePUpNTQ3r168H4Prrr+fRRx9d6gY9KyxJg54K3d3d7Nu3z/53ZWUl3d3dl3BFlx6yLLN161a2bt3KRz/6Ud7//vdz3XXXoes6P/vZz7jzzjsJBAJ2oi0fZTV3Msh8JZxSqRQNDQ34fL4pZ0gJIfiP//gP7r//fh544AGuvPLKi3dxpkB3d7ctug/p++jFF19c9HVcDFwyg7766qvp6+ub9PhXvvIV3v/+9+d9Tr7jweU8h2ihIUkSjz76qH1N/v7v/96mrD7zzDM8+eST3H333Qgh2LNnz5SU1XwSTpFIhJGRkSwJJ8uD+/1+u6lkuix6NBrlU5/6FLqu88c//nHe43MvFG/k++iSGfQTTzwx5+dUVlZy7tw5+99dXV122LiMNCbNnJIkVq5cyQc/+EE++MEPTqKs3n///YyOjrJz5077HJ7LHJNl2Z7mCNndVx0dHQwPDyOEYPXq1aiqmlessLGxkVtvvZXbbruNW2655ZJmsd/I99GSTIpZeOtb35qVFGtoaODGG2+0k2Jvf/vbOXXq1LyTYnfddRf/9m//ZnuUu+++m3e/+90Ltv7LBRZl9ciRIxw5coSuri62bNliE14yKau5z2toaKCwsJCKigq7Fm7Noy4oKODEiRMMDw/z4x//mB/+8IdcccUVl+ATZkPXdTZt2sSTTz7J6tWrueqqq/jP//xPtmzZcqmXNh0u36TYI488wsc//nEGBwd5z3vew44dO3j88cfZsmUL1113HfX19aiqyne/+90LznB/8pOfXEw+7pKE2+3mLW95i91okElZ/epXv0pzczPV1dUcPHiQAwcOsGPHDk6cOIGmaVkCEB6Px5asTaVStLW18cADD9Da2kplZSUdHR1LwqBVVeU73/mOPdfqpptuWurGPGssaQ99sXEJCPaXJUzT5NSpUxw+fJinnnqKJ598kuLiYj70oQ+xb9++vJTVkydP8o//+I989KMf5SMf+QjxeJx4PG6fy5cxZ1y+dejFwl133cVDDz1EMBhk9+7dfOMb35jv3KE/Gfz617+mubmZ66+/3g7RMymrBw4coKOjg4cffpiHHnqIbdu2Xeolv1GwbNAwfTZ93759lJaWIkkSX/jCF+jt7eXBBx+8BKu8vJFJWf3Nb37DK6+8wh/+8IdZCQsuNN7AeZFlg54LcvWUl3F54g18jJqVQf9JM+B7e3vtnx955BG2bt26IK976NAhamtrqamp4atf/eqCvOYyljEb/Ekb9B133MG2bdvYvn07f/zjH/nmN795wa9pGAYf+9jH+O1vf0tjYyM//elPaWxsXIDVLmO2+M53vsP27du56aabGBkZudTLWVQsh9wLjOeff5677rqLxx9/HEh3hwF87nOfu5TLekPhTzQvcvnWoS9nvJF5wksFs2UZ3nLLLbz3ve+9yKtZWviTDrkvBt7IPOHLARcrL3K5YNlDLzAWgyd8iUeyLGnccccdvPrqq0iSRHV1Nd///vcv9ZIWFctn6AXGYvCEL/EEh2VcGiyfoS8F3sg84WUsfczVQy9jCUCSpHZghHTE9H0hxA8u8ZIWDJIkfQi4C6gD9gghXsr4v88BHwEM4P8SQjx+SRa5hLHsoS9PHBRC9EiSVAb8XpKkZiHE4Uu9qAXC68AHgKzDryRJ9cD1wBagAnhCkqRNQghj8Ze4dLGc5b4MIYToGf97AHgE2HNpV7RwEEI0CSFa8vzX+4GHhRBJIUQ7cJo30OdeKCwb9GUGSZJ8kiQFrJ+Bd5D2ahfymg9KkjQgSdLrGY8VS5L0e0mSTo3/fanb0FYD5zL+3TX+2DIysGzQlx9WAs9IknQCOAr8jxDi0AW+5kNA7pCmzwJPCiE2Ak+O/3tBIEnSE5IkvZ7nT34xufGn5XlsOQGUg+Uz9GUGIUQbsKCyH0KIw5IkVec8/H7greM//wh4Crhzgd7v6nk8rQuoyvh3JdCzEOt5I2HZQy9jKqwUQvQCjP99qQdM/Qq4XpIklyRJ64CNpCOUZWRg2aCXsaQgSdK1kiR1AfuB/5Ek6XEAIUQD8DOgETgEfGw5wz0Zy3XoZQAwHnI/JoTYOv7vFuCtQoheSZLKgaeEELWXcInLmAWWPfQypsKvgL8b//nvgEcv4VqWMUsse+hlIEnST0knwEqBfuD/Bn5JOsRdA5wFPiSEGL5Ua1zG7PD/A3jorHwxS4vaAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fe6a5dc1690>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fe6a49aa8d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Environment initialized with bounds X1: ( -10.0 , 10.0 )  X2:( -10.0 , 10.0 )\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " /home/vpreston/anaconda2/lib/python2.7/site-packages/ipykernel_launcher.py:55: RuntimeWarning:divide by zero encountered in log\n",
      " /home/vpreston/anaconda2/lib/python2.7/site-packages/ipykernel_launcher.py:55: RuntimeWarning:invalid value encountered in sqrt\n",
      " /home/vpreston/anaconda2/lib/python2.7/site-packages/ipykernel_launcher.py:55: RuntimeWarning:invalid value encountered in double_scalars\n",
      " /home/vpreston/anaconda2/lib/python2.7/site-packages/ipykernel_launcher.py:55: RuntimeWarning:divide by zero encountered in double_scalars\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fe6a4985b90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fe6a48d4d10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " /home/vpreston/anaconda2/lib/python2.7/site-packages/ipykernel_launcher.py:138: RuntimeWarning:divide by zero encountered in divide\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fe6a44c8090>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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1ikxY5Si5uCs/A4GrVfX3gEmzgHdEZBxQDW941tV4PZT67srQHqArcJuqqoh8infOJg7oDnyU0xdjCpfNmzczaNAg5s+fz4kTJ6hWrRoDBw6kXbt2NG/e3A59fJaj6v/AYLwrQgvcybBVqvofVd0kIu8Dm/EOl3qraopbTx9gPt6Vo0mqusltYiAQJyKxwHogs5EFjAHg0KFDDB48mNdee40yZcrQu3dvmjVrxj/+8Q9Klizpd3jGser/Jl85cuQI119/PQkJCfTq1YsnnniCc845x++wChWr/m8KnH379tGhQwfWr1/Phx9+SPv27f0OyZxBjgaiNya3rVmzhubNm7NhwwY++OADSyz5gCUXk+e99NJLXHHFFZw8eZKlS5fSqVMnv0MyQbDkYvKsY8eO8eijj9K7d2/atGnDF198weWXX571giZPsHMuJk9at24dt9xyCzt27ODee+/lxRdfpGhR+7jmJ9ZzMXlKamoqr732GldeeSUnT55kwYIFvPLKK5ZY8iF7x0yecPz4cd566y0mTJhAQkIC11xzDXFxcZx33nl+h2ZyyHouxjepqals376d9957j6ZNm3LPPfecGpJj0aJFlljyOeu5mFy3b98+nnrqKSZNmsSRI0cAqF69Oh9//DHt2rWzEggFhCUXk2tOnjzJ/Pnz6dGjB0lJSdx66620atWKJk2a0KhRI6uYX8BYcjER9csvv/Dmm28yffp0VqxYwYkTJ2jYsCGffvopDRs29Ds8E0GWXExEnDhxgueff57Y2FgOHTpEo0aN6Nu3L02aNOHmm28mKirK7xBNhOW0QHdFvOpztYBdQBdVPSjewfL/gBjgd+BOVU1wy3QHHnerjVXVN117U2AyUAqIBx7U/Ho3pQHg8OHDtG/fns8++4y2bdsydOhQmjdv7ndYJpcFc7VoMtAmXdsgYJGq1gcWuecAbfFquNTHK0c5AU4lo6F4pS0vB4aKSFr1nglu3rTl0m/L5CP79+/n2muvZeXKlUydOpX4+HhLLIVUlslFVZcBSemaO+IV08b97hTQPsUVCV+FV2WuKnAjsEBVk1T1ILAAaOOmlVPVla63MiVgXSaf+fbbb7nqqqvYvHkzH330EbfddlvWC5kCK6ffc6msqnsB3O+0LyRU58+FuKtn0b47g3aTz3zyySdER0fzww8/MG/ePGJiYvwOyfgs3F+iy6zgdnbbM165yL0islZE1u7bty+HIZpwmzRpEjExMVSvXp21a9dy9dVX+x2SyQNymlx+coc0uN8/u/bMCnSfqf38DNozpKqvqmozVW1m4/X676effqJz58707NmTa665huXLl1OvXj2/wzJ5RE6Tyyy8YtpwelHtWUA38bQADrvDpvlAaxGp4E7ktgbmu2m/iEgLd6WpG1agO1/47LPPuPTSS4mPj+fpp58mPj6ecuXK+R2WyUNyWqB7FPC+iPQEvgP+5WaPx7sMvR3vUnQPAFVNEpERwBo333BVTTtJfD9/XIqe635MHqWqjBs3joEDB1K7dm3mzZvHJZdc4ndYJg+yAt0maKmpqfTv35/x48fTuXNnJk6cSPny5f0Oy+SyYAt0213RJihHjx7ljjvuYPz48fTr148PPvjAEos5I/v6v8nUV199xbJly9iyZQszZ85k586dPPXUUwwaNMjuXDZZsuRi/iQpKYl//etfLF68GIDixYvTqlUrXnjhBfv+igmaJRdzmt27dxMTE8PXX3/N6NGj6dy5MzVr1rQykybb7BNjTlm8eDFdu3bl6NGjzJkzh+uuu87vkEw+Zid0DarKmDFjuOGGGzjnnHNYs2aNJRYTMuu5FHK//vor3bt3Z8aMGXTp0oWJEydSpkwZv8MyBYAll0Ls0KFDtG3bljVr1jBu3Dj69etnV4FM2FhyKaT2799P69atSUxMZNq0aTZEqgk7Sy6F0M6dO2nfvj07duxg1qxZtGlj9blM+FlyKWSWL19Ohw4dUFXi4+O55ppr/A7JFFCWXAqRtWvXEhMTQ7Vq1ZgzZw5169b1OyRTgNml6EIgJSWF4cOHc+WVV1KpUiUWLVpkicVEXEjJRUT6i8gmEUkUkXdFpKSI1BaRz0Vkm4i8JyLF3bwl3PPtbnqtgPUMdu1ficiNob0kEyglJYUePXowdOhQOnXqxIoVK6he3SqJmsjLcXIRkepAX6CZG3KkCNAVGA0860YGOAj0dIv0BA6qaj3gWTcfItLALXcxXuX/l0SkSE7jMn9ITU3l7rvv5q233mLEiBHExcVRtWpVv8MyhUSoh0VFgVIiUhSIAvYC1wLT3PT0IwOkjRgwDbjOVZ/rCMSp6nFV3YlXaOryEOMq9FJTU7nvvvuYPHkyw4YN4/HHH896IWPCKMfJRVX3AGPxKtHtBQ4D64BDqprsZgus5n9qBAA3/TBQicxHBvgTK9AdnNTUVHr16sXrr7/O448/zhNPPOF3SKYQCuWwqAJer6M2UA0ojTcoWnpppe5CHgHACnRnTVV54IEHeOWVVxg8eDDDhw+3b90aX4RyWHQ9sFNV96nqSWAGcAXeQGhpl7gDq/mfGgHATS+PN9haZiMDmGxKTk6mV69evPTSSwwYMICRI0daYjG+CSW5fAe0EJEod+7kOmAz8CnwTzdP+pEB0kYM+Cew2I2yOAvo6q4m1cYb0nV1CHEVSqmpqXTp0oWXX36ZwYMHM3r0aEssxlc5/hKdqn4uItOABCAZWA+8CswB4kQk1rVNdItMBN4Ske14PZaubj2bROR9vMSUDPRW1ZScxlVYTZkyhQ8//JAxY8YwYMAAv8Mxxqr/FwQHDhygQYMG1K1bl+XLl3PWWfbdSBM5Vv2/EEhJSWHevHk0a9aMpKQkXnjhBUssJs+wT2I+NWfOHOrVq0fbtm1RVZYtW0Z0dLTfYRlziiWXfGjYsGHcdNNNREVFERcXx9atW2nZsqXfYRlzGrsrOp+JjY3lySefpEePHkyYMIESJUr4HZIxGbLkko+MGTOGIUOG0K1bN15//XU7v2LyNPt05hPPPfccAwcOpGvXrkyaNMkSi8nz7BOaD8TFxdG/f386d+7MlClTKFLEbho3eZ8llzzu2LFjDBgwgMsuu4x33nmHYsWK+R2SMUGxcy553Guvvcbu3bt54403KF68uN/hGBM067nkYatWrWLIkCFcffXVNgKiyXcsueRRK1euPDW86ptvvmk3IZp8xw6L8qDExERiYmKoUqUKS5cupVq1an6HZEy2hVqg+2wRmSYiW0Vki4i0FJGKIrLAFehe4IpKIZ7xrhD3FyISHbCe7m7+bSLSPfMtFnyqSs+ePSlZsiQLFiywxGLyrVAPi/4HzFPVvwKNgS3AIGCRK9C9yD0Hr0pdffdzLzABQEQqAkOB5ni1c4emJaTCaPbs2axevZrY2Fhq1arldzjG5FgoZS7LAVfh6rWo6glVPcTphbjTF+ieop5VeBXrqgI3AgtUNUlVDwIL8EYBKHSOHDnC4MGDqVevHt26dfM7HGNCEso5lzrAPuANEWmMV5z7QaCyqu4FUNW9InKemz+zQtxBF+guyA4dOkSbNm3YunUrs2bNsu+zmHwvlMOiokA0MEFVLwV+449DoIyEXKC7oFb/P3DgANdddx0JCQlMmzaNmJgYv0MyJmShJJfdwG5V/dw9n4aXbH5yhzu43z8HzJ9RIe6gC3QXxOr/P//8M9deey2bNm1i5syZdOrUKeuFjMkHQhm36EfgexG50DWlFegOLMSdvkB3N3fVqAVw2B0+zQdai0gFdyK3tWsr8Hbt2kWrVq3Ytm0bH3/8sfVYTIES6vdcHgCmuvGgvwF64CWs90WkJ94IAf9y88YDMXgjKv7u5kVVk0RkBLDGzTdcVZNCjCvP27BhA61bt+bkyZPMnTuXq6++2u+QjAkrK9DtgwMHDtCsWTOSk5NZuHAhF154YdYLGZNHBFug276hm4t2795NXFwcEydO5IcffuCzzz6zxGIKLLu3KJeMGTOGGjVqMGDAAMqVK8e0adO4/PLL/Q7LmIixnksuiI+PZ9CgQXTq1IkxY8ZQr149v0MyJuIsuUTY0qVL6dKlC40bN+btt98mKirK75CMyRV2WBRBmzZtom3bttSoUYP4+HhLLKZQsZ5LhKgq/fv3p0SJEixevJgqVar4HZIxucqSS4TMnj2bBQsW8Oyzz1piMYWSHRZFQGJiIt26daNBgwb06tXL73CM8YUllzA7evQo7dq1o1SpUsTHx1tRbVNo2WFRmL344ot89913fPrpp9SsWdPvcIzxjfVcwujQoUM8/fTT3HjjjbRq1crvcIzxlSWXMPn1119p3749hw8f5qmnnvI7HGN8Z8klDH799VdiYmJYuXIl77zzDtHR0VkvZEwBF3JyEZEiIrJeRGa757VF5HNXyf89V44BESnhnm9302sFrGOwa/9KRG4MNabc9Ntvv9GuXTtWrFjB1KlT6dKli98hGZMnhKPn8iBe1f80o4FnXfX/g0BP194TOKiq9YBn3XyISAOgK3AxXmHul0QkX4y0/vvvv3PTTTexfPlypk6dyi233OJ3SMbkGaGOW3Q+0A543T0X4Fq8kpfw5+r/aaMCTAOuc/N3BOJU9biq7sQrJpUvbheOjY1l6dKlvP3225ZYjEkn1J7Lc8CjQKp7Xgk4pKrJ7nlgJf9TVf7d9MNu/qCr/+elAt379+/n+eef55ZbbuHWW2/1NRZj8qJQxi26CfhZVdcFNmcwq2YxLejq/3mpQPfo0aP57bffGDJkiK9xGJNXhfIlur8BHUQkBigJlMPryZwtIkVd7ySwkn9alf/dIlIUKA8kkY3q/3nF5MmTGTt2LHfddRcNGjTwOxxj8qRQqv8PVtXzVbUW3gnZxap6O/Ap8E83W/rq/2mjAvzTza+uvau7mlQbb7jX1TmNK9I2bdrE3XffzQ033MBLL73kdzjG5FmR+Pr/QCBORGKB9bjhXt3vt0RkO16PpSuAqm4SkffxhiVJBnqrakoE4gqLoUOHEhUVxbvvvkuJEiX8DseYPCssyUVVlwBL3ONvyOBqj6oe449hRtJPGwmMDEcskZSQkMD06dMZOnQolSpV8jscY/I0+4ZuNjzxxBNUqFCB/v37+x2KMXmeJZcgrVq1ijlz5jBgwADKly/vdzjG5HmWXII0ZMgQzj33XB544AG/QzEmX7B6LkFYsmQJCxcu5JlnnqFMmTJ+h2NMvmA9lyyoKkOGDKFq1arcf//9fodjTL5hPZcszJ07l+XLl/P8889TqlQpv8MxJt+wgejP4OTJk1xyySWkpKSQmJho9XCNwQaiD4tXX32VrVu3MnPmTEssxmSTnXPJxMGDBxk6dCitWrWiQ4cOfodjTL5jySUTI0eOJCkpiXHjxuGVnTHGZIcllwxs376d8ePHc+edd3LppZf6HY4x+ZIllwwMHDiQ4sWLExsb63coxuRboRSL+ouIfCoiW0Rkk4g86NorisgCV6B7gYhUcO0iIuNdIe4vRCQ6YF3d3fzbRKR7ZtvMDe+//z4zZsxg4MCBVKtWzc9QjMnXQum5JAMPq+pFQAugtyu2PQhY5Ap0L3LPAdri1WqpD9wLTAAvGQFDgeZ4d1MPTUtIue3dd9/ltttu429/+xuPPPKIHyEYU2CEUixqr6omuMe/4I0AUJ3TC3GnL9A9RT2r8CrWVQVuBBaoapKqHgQW4I0CkGtUlYceeojbbruNli1bMnfuXPvCnDEhCss5FzcG0aXA50BlVd0LXgICznOzZVaIO+gC3ZGyZMkSnn32We677z4WLVpE2bJlc3PzxhRI4RgUrQwwHeinqkfONGsGbdkq0B2p6v/Dhw+natWqPPfcc/ZlOWPCJNRxi4rhJZapqjrDNf/kDndwv3927ZkV4g66QHckqv8vW7aMJUuWMHDgQEqWLBmWdRpjQrtaJHh1cbeo6riASYGFuNMX6O7mrhq1AA67w6b5QGsRqeBO5LZ2bblixIgRVK5cmXvuuSe3NmlMoRDq0CL/Br4UkQ2u7f+AUcD7ItIT+I4/6ubGAzF4Iyr+DvQAUNUkERkBrHHzDVfVpBDiCtqKFStYuHAhY8eOJSoqKjc2aUyhUajvim7bti1r165l165dlC5dOkyRGVPy9Y5XAAAGC0lEQVSwBXtXdKH9hu7q1auZN28ejzzyiCUWYyKg0CaXESNGULFiRXr16uV3KMYUSIUyuSQkJDB79mweeugh+06LMRFSKJPLiBEjOPvss+nTp4/foRhTYBW65LJx40ZmzpxJv379bPwhYyKo0CWX2NhYypUrR9++ff0OxZgCrVAll8TERKZNm0bfvn2pUMGXG6+NKTQKVXIZOXIkZcqUoV+/fn6HYkyBV2iSy9atW3nvvffo06cPlSpV8jscYwq8QpNcRo4cSalSpXjooYf8DsWYQqFQJJdt27bxzjvv0KtXL8J1N7Ux5swKRXIZNWoUxYsX5+GHH/Y7FGMKjQKfXHbt2sWUKVO45557qFKlit/hGFNo5JnkIiJtROQrNzrAoKyXCM6YMWMQER599NFwrdIYE4Q8kVxEpAjwIt4IAQ2AW91IAiHZs2cPEydOpEePHpx//vmhrs4Ykw15IrngDSmyXVW/UdUTQBzeaAEhGTt2LCkpKQwaFLaOkDEmSHkluYR9BIBjx44xZcoU7rjjDmrXrh1ScMaY7AulzGU4BTUCgIjcizegGjVq1DjjCkuWLMmmTZtISUkJS4DGmOzJKz2XoEYAyG71/ypVqlC9eq4OgWSMcfJKclkD1BeR2iJSHOiKN1qAMSafyhOHRaqaLCJ98IYUKQJMUtVNPodljAlBnkguAKoajzf8iDGmAMgrh0XGmALGkosxJiLy7aBoIrIP+DaIWc8B9kc4nGBYHKezOE6Xn+KoqapZXq7Nt8klWCKyNpjR4SwOi8PiCG8cdlhkjIkISy7GmIgoDMnlVb8DcCyO01kcpytwcRT4cy7GGH8Uhp6LMcYHBTa5RKqyXRDb/YuIfCoiW0Rkk4g86NqHicgeEdngfmJyIZZdIvKl295a11ZRRBaIyDb3O6Kjw4nIhQGveYOIHBGRfrm1P0Rkkoj8LCKJAW0Z7gPxjHefmS9EJDrCcfxXRLa6bX0oIme79loicjRg37wc4TgyfS9EZLDbH1+JyI3Z2piqFrgfvPuTdgB1gOLARqBBLm27KhDtHpcFvsarrjcMeCSX98Mu4Jx0bWOAQe7xIGB0Lr8vPwI1c2t/AFcB0UBiVvsAiAHm4pUAaQF8HuE4WgNF3ePRAXHUCpwvF/ZHhu+F+9xuBEoAtd3fVJFgt1VQey4RqWwXDFXdq6oJ7vEvwBZCLHwVZh2BN93jN4FOubjt64AdqhrMlx/DQlWXAUnpmjPbBx2BKepZBZwtIlUjFYeqfqKqye7pKrxSIxGVyf7ITEcgTlWPq+pOYDve31ZQCmpyCXtlu5wQkVrApcDnrqmP6wJPivThiKPAJyKyzhXaAqisqnvBS4TAebkQR5quwLsBz3N7f6TJbB/4+bm5C6/XlKa2iKwXkaUi8vdc2H5G70VI+6OgJpegKttFNACRMsB0oJ+qHgEmAHWBJsBe4JlcCONvqhqNV/i8t4hclQvbzJCr09MB+MA1+bE/suLL50ZEHgOSgamuaS9QQ1UvBR4C3hGRchEMIbP3IqT9UVCTS1CV7SJFRIrhJZapqjoDQFV/UtUUVU0FXiMb3cucUtUf3O+fgQ/dNn9K6+q73z9HOg6nLZCgqj+5mHJ9fwTIbB/k+udGRLoDNwG3qzvR4Q5DDrjH6/DOdVwQqRjO8F6EtD8KanLxrbKdiAgwEdiiquMC2gOP3W8GEtMvG+Y4SotI2bTHeCcPE/H2Q3c3W3fgo0jGEeBWAg6Jcnt/pJPZPpgFdHNXjVoAh9MOnyJBRNoAA4EOqvp7QPu54g23g4jUAeoD30Qwjszei1lAVxEpISK1XRyrg15xJM5I54UfvDP/X+Nl/cdycbtX4nUdvwA2uJ8Y4C3gS9c+C6ga4Tjq4J3p3whsStsHQCVgEbDN/a6YC/skCjgAlA9oy5X9gZfQ9gIn8f4T98xsH+AdBrzoPjNfAs0iHMd2vHMaaZ+Tl928nd17thFIANpHOI5M3wvgMbc/vgLaZmdb9g1dY0xEFNTDImOMzyy5GGMiwpKLMSYiLLkYYyLCkosxJiIsuRhjIsKSizEmIiy5GGMi4v8DVD3l2vKv+sMAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fe6a49db450>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fe6a4b26c90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fe6a5c655d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fe6a5dcff90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fe6a5ecacd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create a random enviroment sampled from a GP with an RBF kernel and specified hyperparameters, mean function 0 \n",
    "# The enviorment will be constrained by a set of uniformly distributed  sample points of size NUM_PTS x NUM_PTS\n",
    "\n",
    "''' Options include mean, info_gain, and hotspot_info'''\n",
    "reward_function = 'info_gain'\n",
    "\n",
    "world = Environment(ranges = (-10., 10., -10., 10.), # x1min, x1max, x2min, x2max constraints\n",
    "                    NUM_PTS = 20, \n",
    "                    variance = 100.0, \n",
    "                    lengthscale = 3.0, \n",
    "                    visualize = True,\n",
    "                    seed = 1)\n",
    "\n",
    "evaluation = Evaluation(world)\n",
    "\n",
    "# Gather some prior observations to train the kernel (optional)\n",
    "ranges = (-10, 10, -10, 10)\n",
    "x1observe = np.linspace(ranges[0], ranges[1], 5)\n",
    "x2observe = np.linspace(ranges[2], ranges[3], 5)\n",
    "x1observe, x2observe = np.meshgrid(x1observe, x2observe, sparse = False, indexing = 'xy')  \n",
    "data = np.vstack([x1observe.ravel(), x2observe.ravel()]).T\n",
    "observations = world.sample_value(data)\n",
    "\n",
    "# Create the point robot\n",
    "robot = Nonmyopic_Robot(sample_world = world.sample_value, \n",
    "              start_loc = (0.0, 0.0, 0.0), \n",
    "              ranges = (-10., 10., -10., 10.),\n",
    "              kernel_file = None,\n",
    "              kernel_dataset = None,\n",
    "              prior_dataset =  None, \n",
    "              init_lengthscale = 3.0, \n",
    "              init_variance = 100.0, \n",
    "              noise = 0.05,\n",
    "              path_generator = 'equal_dubins',\n",
    "              frontier_size = 20, \n",
    "              horizon_length = 5.0, \n",
    "              turning_radius = 0.5, \n",
    "              sample_step = 2.0,\n",
    "              evaluation = evaluation, \n",
    "              f_rew = reward_function,\n",
    "              computation_budget = 2.0,\n",
    "              rollout_length = 3)\n",
    "\n",
    "robot.nonmyopic_planner(T = 150)\n",
    "robot.visualize_world_model()\n",
    "robot.visualize_trajectory()\n",
    "robot.plot_information()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fe6a4b26510>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fe6a5f062d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Environment initialized with bounds X1: ( -10.0 , 10.0 )  X2:( -10.0 , 10.0 )\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " /home/vpreston/anaconda2/lib/python2.7/site-packages/ipykernel_launcher.py:55: RuntimeWarning:divide by zero encountered in log\n",
      " /home/vpreston/anaconda2/lib/python2.7/site-packages/ipykernel_launcher.py:55: RuntimeWarning:invalid value encountered in sqrt\n",
      " /home/vpreston/anaconda2/lib/python2.7/site-packages/ipykernel_launcher.py:55: RuntimeWarning:invalid value encountered in double_scalars\n",
      " /home/vpreston/anaconda2/lib/python2.7/site-packages/ipykernel_launcher.py:55: RuntimeWarning:divide by zero encountered in double_scalars\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fe6a4b24e90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fe6a4a77750>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " /home/vpreston/anaconda2/lib/python2.7/site-packages/ipykernel_launcher.py:138: RuntimeWarning:divide by zero encountered in divide\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fe6a4a777d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fe697fc1ad0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fe697f90050>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fe697f54590>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fe697f18ad0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fe697ed9850>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create a random enviroment sampled from a GP with an RBF kernel and specified hyperparameters, mean function 0 \n",
    "# The enviorment will be constrained by a set of uniformly distributed  sample points of size NUM_PTS x NUM_PTS\n",
    "\n",
    "''' Options include mean, info_gain, and hotspot_info'''\n",
    "reward_function = 'mean'\n",
    "\n",
    "world = Environment(ranges = (-10., 10., -10., 10.), # x1min, x1max, x2min, x2max constraints\n",
    "                    NUM_PTS = 20, \n",
    "                    variance = 100.0, \n",
    "                    lengthscale = 3.0, \n",
    "                    visualize = True,\n",
    "                    seed = 1)\n",
    "\n",
    "evaluation = Evaluation(world)\n",
    "\n",
    "# Gather some prior observations to train the kernel (optional)\n",
    "ranges = (-10, 10, -10, 10)\n",
    "x1observe = np.linspace(ranges[0], ranges[1], 5)\n",
    "x2observe = np.linspace(ranges[2], ranges[3], 5)\n",
    "x1observe, x2observe = np.meshgrid(x1observe, x2observe, sparse = False, indexing = 'xy')  \n",
    "data = np.vstack([x1observe.ravel(), x2observe.ravel()]).T\n",
    "observations = world.sample_value(data)\n",
    "\n",
    "# Create the point robot\n",
    "robot = Nonmyopic_Robot(sample_world = world.sample_value, \n",
    "              start_loc = (0.0, 0.0, 0.0), \n",
    "              ranges = (-10., 10., -10., 10.),\n",
    "              kernel_file = None,\n",
    "              kernel_dataset = None,\n",
    "              prior_dataset =  None, \n",
    "              init_lengthscale = 3.0, \n",
    "              init_variance = 100.0, \n",
    "              noise = 0.05,\n",
    "              path_generator = 'equal_dubins',\n",
    "              frontier_size = 20, \n",
    "              horizon_length = 5.0, \n",
    "              turning_radius = 0.5, \n",
    "              sample_step = 2.0,\n",
    "              evaluation = evaluation, \n",
    "              f_rew = reward_function,\n",
    "              computation_budget = 2.0,\n",
    "              rollout_length = 3)\n",
    "\n",
    "robot.nonmyopic_planner(T = 150)\n",
    "robot.visualize_world_model()\n",
    "robot.visualize_trajectory()\n",
    "robot.plot_information()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fe697d94610>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fe697d6f050>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Environment initialized with bounds X1: ( -10.0 , 10.0 )  X2:( -10.0 , 10.0 )\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " /home/vpreston/anaconda2/lib/python2.7/site-packages/ipykernel_launcher.py:55: RuntimeWarning:divide by zero encountered in log\n",
      " /home/vpreston/anaconda2/lib/python2.7/site-packages/ipykernel_launcher.py:55: RuntimeWarning:invalid value encountered in sqrt\n",
      " /home/vpreston/anaconda2/lib/python2.7/site-packages/ipykernel_launcher.py:55: RuntimeWarning:invalid value encountered in double_scalars\n",
      " /home/vpreston/anaconda2/lib/python2.7/site-packages/ipykernel_launcher.py:55: RuntimeWarning:divide by zero encountered in double_scalars\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fe696b23b50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fe697d9db90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " /home/vpreston/anaconda2/lib/python2.7/site-packages/ipykernel_launcher.py:138: RuntimeWarning:divide by zero encountered in divide\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fe6a5f7a310>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fe6a40356d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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XZ0LI0dtvwxVXwMsvw7vv+ujs228P48fD0UcXO7r8iyQSQg5WrfIrLu++6+OA9O4Ne+8NP/4xbLppsaMrjEgiIeTg3HO98fSppzyBNEXRJhJCHT38MNx8syeSpppAIJJICHUyd653Ye/Wza/CNGWRREKopbVr/Vb+zz6De+9tOm0f1Yk2kRBq6fLL4dlnYezYhjvJdiFFTSSEWhg3Di69FAYM8EmmQiSRELL2yiswcCAccIDPFdNYbuXPVSSRELKwYIH3B9luO59carPNih1Rw5HNvDNjJS2SNC2jbJik9yW9mh59M967QFK5pBmSDsko75PKyiUNqbydEBqy886DDz/0eWLatq15+aYkm5rI7fjE25WNMLOu6fE4gKTdgP7A7ukzN0lqJqkZMAo4FNgNOC4tG0KD9+yzfgfu+efDN79Z7GganmzmnXkuTeSdjSOA+8zsM+AdSeXAPum9cjObBSDpvrTsG7WOOIQCWrUKBg2Czp19iofwZbm0iQyWNDWd7rRMZR2AORnLzE1l1ZWH0KBdfLGPBXLrrdC8ebGjaZjqmkRuBnYBugLzgWtTeVXt1baB8i+RNEhSmaSyxYsX1zG8EHI3fDhcey2cdlrjHQukPtQpiZjZQjNba2brgFv44pRlLtAxY9EdgHkbKK9q3WPMrIeZ9WjTpk1dwgshZ9dcA//zPz61ww03FDuahq1OSURS+4yXRwEVV24mAP0lbSZpJ6AL8DI+iXcXSTtJ2hRvfJ1Q97BDyJ+HH/YEcswx3qDa1Lu116TGhlVJ9wK9gNaS5gJDgV6SuuKnJLOBUwHMbLqkcXiD6RrgTDNbm9YzGHgKaAaMNbPp9b43IeRo9mzvULb33nDHHaU90XahyKzKpokGoUePHlZWVlbsMEITsW4dfPe7Ppzhq6/CTjsVO6LikjTZzHrUtFzcgBdC8tBD8M9/+o11TT2B1EZ0ew9NlpnXOFav9udXXOF35Q4YUOzISkvUREKT9PnnMHgw3HIL9OgB3bt7QrnttmgHqa1IIqFJGjrUE8iAAfDXv8LUqdC/P5xwQrEjKz2RREKTs2qV38r/ox/5FZhVq3y0shYtih1ZaYokEpqcv/wFliyBM8/015tvXtx4Sl00rIYmZd0674H6jW/4PDEhd1ETCU3K6NEwZYqfxsTIZPUjaiKhyViwwG/n//734cQTix1N4xFJJDQZo0fDRx/BqFFRC6lPkURCk7BmjV/SPeSQmOahvkUSCU3CX/8K778Pp55a7Egan0giodFbu9YnnNphBzjssGJH0/jE1ZnQ6P3xjzB5MtxzD2wcf/H1LmoioVFbtgx++1sf3rB//2JH0zhFEgmN2g03eCK59tq4IpMvkURCo7V8OVx/PRx5JOy5Z7GjabwiiYRGa+RIr4VcckmxI2ncIomERmn5chgxAvr1g732KnY0jVskkdDofP45XHQRLF0atZBCiAteoVH58EM44ACYMQNOOcVHLAv5VWNNJE2TuUjStIyyVpImSnor/WyZyiVppKTyNMVmt4zPDEzLvyVpYH52JzR1Z54Js2bBhAnezT3kXzanM7cDfSqVDQEmmVkXYFJ6DXAoPmFVF2AQPt0mklrh89Xsi8+WNzRj/t4Q6sV998H998OwYXD44XFJt1BqTCJm9hywpFLxEcAd6fkdwJEZ5XeaexHYJs2Wdwgw0cyWmNlSYCJfTkwh1Nn778MZZ8B++/nsdaFw6tqw2s7M5gOkn21TeQdgTsZyc1NZdeUh5OyTT+D44+Gzz+DOO6Nre6HV9+GuqgJpGyj/8gqkQfipEJ06daq/yEKjsG4d/Pvf0KoVrFzpo7Tfdhv8619w113QpUuxI2x66ppEFkpqb2bz0+nKolQ+F+iYsdwOwLxU3qtS+TNVrdjMxgBjwKfRrGN8oZFYuhRmzvS5cdesgZNP9hvpMm21Ffz5z14bCYVX1yQyARgIXJV+PpJRPljSfXgj6vKUaJ4CfpfRmNobuKDuYYem4KqrvJ/H6tXwgx/AokXw2mte1qULNG/u3dl32gk2ih5PRVNjEpF0L16LaC1pLn6V5SpgnKRTgPeAY9LijwN9gXLgE+BkADNbIuly4JW03GVmVrmxNoT/V14OF1/syaNnT59savvtYfx4OProYkcXMsms4Z4x9OjRw8rKyoodRiiCn/wEHnvMk0n79vDBB37asummxY6s6ZA02cx61LRcVAJDgzNzJowbB7/+tScQgNatI4E0VJFEQoNz661+mbZihrrQsEUSCQ3KZ5/5Jdt+/WC77YodTchGJJHQoDz4oLd/DBpU7EhCtiKJhLwy8/4d2S579dXw9a/7VZlQGiKJhLxZu9ZvhNttN7+3pSZPPun9QIYMiX4fpSR+VSFvhg71SaPefRd69/ahCjfkd7+Djh2j52mpiSQS8uK11zwpnHyy1zBmzoQBA/zel6o8/zy88AL85jdxKbfURBIJefGb30DLlnDddXDQQT7e6aOPwnnnVZ1I/vd/oU0bH40slJa4aTrUu0cegYkTPXFss42XnXmmD1k4YgTMmQM33+wdyADuvhueeAKuvNLvhwmlJbq9h3o1ezZ06wadOsFLL8Fmm33xnhlccw1ceCFsvbXXVlavhksvhQMP9ESSuXwormy7vUdNJNSrn/3MT1ceeODLCUHyxNGnD5x7rl+FATjkEB/WMBJIaYokEurNc8/BP/7hs8599avVL/fNb8LTT8Obb3ri2HnnwsUY6l8kkVBvrrgC2rbNvrfprrvmN55QGHF1JtSLJ5/0xtTf/AY237zY0YRCiiQScrZyJZx6qtcszjqr2NGEQoskEurso4/8ku3uu8N77/lkUdE42vREEgm18vHHfkPd/ffDjjv6wEE77ghPPeXTV4amJxpWQ1b++U846ihYvNgv1Zr5RFEjRvjP0HRFEilxzz3nAxfvvTf89rf5qQ0sXw4nnAAtWnjN47PPfN6X006DTTap/+2F0hJJpIRNngyHHQbbbus3vB18sE9k3bt3/W7nV7/yruovvAD771+/6w6lL6c2EUmzJb0u6VVJZamslaSJkt5KP1umckkaKalc0lRJ3epjB5qqN97wnp7bbutf7qlTfTCfQw6Bzp39DtpVq3Lfzl//6sMVDhkSCSRUrT4aVg8ys64ZfeyHAJPMrAswKb0GOBTokh6DgJvrYdtNxttv+xf5uOP857e/7acSEydChw5+M9szz8Dw4fCNb8BFF3lZnz4wb17dtrl0KfziF97D9JJL6nV3QmNiZnV+ALOB1pXKZgDt0/P2wIz0fDRwXFXLVffo3r27NWVLlpjdeqtZ795mYNasmdl22/nzH/7QbObM6j/73HNmZ59t1qKF2R57+Lpq68QTfZuTJ9d9H0LpAsosmzyQzULVfhjeAaYAk4FBqWxZpWWWpp+PAd/JKJ8E9NjQ+ptyEnn5ZbMOHfw31KmT2aWXms2da7ZundmHH2a/nr/9zWzTTc0OOMDs44+z/9zo0b7tSy6pfeyhccg2ieTasHqAmc2T1BaYKOm/G1hWVZR9aRwCSYPw0x06deqUY3ilacoUnzqybVtv7/j2t/2yaoVWrbJf18EH+3gdxx7rV1gefHD9dVW2fLmP6zF8uJ8KXXRR3fcjNA05tYmY2bz0cxHwELAPsFBSe4D0c1FafC7QMePjOwBfOls3szFm1sPMerRp0yaX8ErS4sVw5JHenvHii37JdkNf+mz8+Mc+ivrDD/voYuAdxm65xQdR7tTJk9ZPf+odx4YP9xHGHnkkhioMNatzEpG0haQtK54DvYFpwARgYFpsIPBIej4BGJCu0uwHLDez+XWOvJG68EJYsAAeeqh+J2865xxPGL/6FfzhD9C1q99tu9VWXltZudIHBerXD8rKfBa6SCAhG7mczrQDHpL/m9wYuMfMnpT0CjBO0inAe8AxafnHgb5AOfAJcHIO226U/vtfGDvWb2Lr3r1+173JJnDjjdC3L/zylz7ex/jx3gs115pOaNpieMQGYt067zj2wgt+OTdfZ3IrV3q7R7t2Pt9tCNWJ4RFLzFVX+enEyJH5SyDgXddbtMjf+kPTE3fxNgCTJvl9L8cfD4MHFzuaEGonkkiRLVjgl1533RXGjIn2iVB64nSmiNau9drHihVeG9lii2JHFELtRRIpoiuu8NHRb7vNRwcLoRTF6UyR/Oc/cPnl3sHrpJOKHU0IdRdJJI/mzfMBfCpbs8bvjm3dGm64ofBxhVCfIonkycyZPinTDjt4J69MQ4f6gEIjR9buPpgQGqJIInlg5r1ON9vMx+I46yx4/nl/7557fMCgn//cb4oLodRFEslCeTmcfz707w/Tpm142dWr4eKLfZrIyy/3G946d4YBA/y+lBNO8Lty//CHgoQeQt7F1ZkaLFniN6jNn+8zuz3+uCeHXr28e/qf/wwvv+x337Zu7V3KP/kETjwRzjjDu5bfdps3oL72mt9gN3Ro3NwWGo9IIhtg5qcd8+f7lAnt23tt5JxzvlimXTsfd6NdO/jwQ78r9nvf8/tgKvTqBXPnFjz8EAoiksgG3Hyz35J/7bU+JQP4DXLTpsH06bD99j7nSkybEJqySCLVeOwxn2Olb9/1ax4Ae+zhjxBCNKx+yaJFPqL64Yf7mBu33w4bxVEKoVrx9UjM4K67/Ea48ePh0ku9L0cTHKExhFpp0qczK1f6IMaPPuoNp8uW+QRNt97qQwmGEGrWKJPImjU+zOBXvuL9M6pyxx1w7rl+RWWXXbzjV8+e8JOfQLNmhY03hFLW6JLIp5/CgQf6YMPgfTuOOeaL982849jw4b7c1Vf7FZYYxyOEuml0bSIPPOAJ5KabfLqFgQN9ntoKo0d7Ajn9dPj73/30JRJICHXX6AZq/s53YOFCmDEDPvjAp0bYemt45RUf+OeYY+D73/d2kDhtCaF62Q7UXPCaiKQ+kmZIKpc0pOZPZG/aNG8gPfVUvyzbtq03nM6Y4R3DjjrKk8pdd0UCCaG+FLRNRFIzYBTwA3xGvFckTTCzN3Jdt5k3lDZvvv4gPwcd5KOH3XOPDz94xRW+TAihfhS6YXUfoNzMZgFIug84Asg5iYwa5XfO3nST3wiXqWdPf4QQ6l+hT2c6AHMyXs9NZXW2bh0MG+ZjdvTpA6edlsvaQgi1VegkUtV1kPVadiUNklQmqWzx4sU1rrCsDC67zK/CPPRQXGkJodAKfTozF+iY8XoHYF7mAmY2BhgDfnWmphXus49feenWLRJICMVQ6JrIK0AXSTtJ2hToD0zIdaXdu0cCCaFYCloTMbM1kgYDTwHNgLFmNr2QMYQQ6lfBu72b2ePA44XebgghPxpdt/cQQmFFEgkh5CSSSAghJw36BjxJi4F3s1y8NfBBHsPJVsSxvohjfaUUx45mVuPYfg06idSGpLJs7jiMOCKOiKN+44jTmRBCTiKJhBBy0piSyJhiB5BEHOuLONbX6OJoNG0iIYTiaEw1kRBCEZR8EsnncIs1bLejpH9IelPSdElnp/Jhkt6X9Gp69C1ALLMlvZ62V5bKWkmaKOmt9LNlnmP4esY+vypphaRzCnE8JI2VtEjStIyyKvdfbmT6e5kqqVue4xgu6b9pWw9J2iaVd5a0KuO4/DHPcVT7e5B0QToeMyQdUusNmlnJPvCb+N4GdgY2BV4DdivQttsD3dLzLYGZwG7AMOC8Ah+H2UDrSmW/B4ak50OAqwv8e1kA7FiI4wF8F+gGTKtp/4G+wBP42Db7AS/lOY7ewMbp+dUZcXTOXK4Ax6PK30P6m30N2AzYKX2fmtVme6VeE/n/4RbN7HOgYrjFvDOz+WY2JT3/CHiTHEdpq2dHAHek53cARxZw2wcDb5tZth0Fc2JmzwFLKhVXt/9HAHeaexHYRlL7fMVhZk+b2Zr08kV8DJ28quZ4VOcI4D4z+8zM3gHK8e9V1ko9idT7cIt1IakzsBfwUioanKqvY/N9GpEY8LSkyZIGpbJ2ZjYfPOEBbQsQR4X+wL0Zrwt9PKD6/S/m38zP8FpQhZ0k/UfSs5IOLMD2q/o95Hw8Sj2J1DjcYt4DkFoA44FzzGwFcDOwC9AVmA9cW4AwDjCzbsChwJmSvluAbVYpDTbVD/hLKirG8diQovzNSLoIWAPcnYrDheBUAAABpklEQVTmA53MbC/g18A9krbKYwjV/R5yPh6lnkRqHG4xnyRtgieQu83sQQAzW2hma81sHXALtawa1oWZzUs/FwEPpW0urKimp5+L8h1HcigwxcwWppgKfjyS6va/4H8zkgYChwEnWGqISKcPH6bnk/G2iK/lK4YN/B5yPh6lnkTyMtxiNiQJ+BPwppldl1GeeX59FDCt8mfrOY4tJG1Z8RxvyJuGH4eBabGBwCP5jCPDcWScyhT6eGSobv8nAAPSVZr9gOUVpz35IKkPcD7Qz8w+yShvI5+HCUk7A12AWXmMo7rfwwSgv6TNJO2U4ni5VivPR+twIR94a/tMPJNfVMDtfgev9k0FXk2PvsCfgddT+QSgfZ7j2BlvXX8NmF5xDIBtgUnAW+lnqwIck+bAh8DWGWV5Px540poPrMb/s55S3f7j1fdR6e/ldaBHnuMox9scKv5G/piW/VH6fb0GTAEOz3Mc1f4egIvS8ZgBHFrb7UWP1RBCTkr9dCaEUGSRREIIOYkkEkLISSSREEJOIomEEHISSSSEkJNIIiGEnEQSCSHk5P8AIzAOOdd4KBYAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fe6a406cc10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fe697f8e190>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fe697f536d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fe697fe0450>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create a random enviroment sampled from a GP with an RBF kernel and specified hyperparameters, mean function 0 \n",
    "# The enviorment will be constrained by a set of uniformly distributed  sample points of size NUM_PTS x NUM_PTS\n",
    "\n",
    "''' Options include mean, info_gain, and hotspot_info'''\n",
    "reward_function = 'hotspot_info'\n",
    "\n",
    "world = Environment(ranges = (-10., 10., -10., 10.), # x1min, x1max, x2min, x2max constraints\n",
    "                    NUM_PTS = 20, \n",
    "                    variance = 100.0, \n",
    "                    lengthscale = 3.0, \n",
    "                    visualize = True,\n",
    "                    seed = 1)\n",
    "\n",
    "evaluation = Evaluation(world)\n",
    "\n",
    "# Gather some prior observations to train the kernel (optional)\n",
    "ranges = (-10, 10, -10, 10)\n",
    "x1observe = np.linspace(ranges[0], ranges[1], 5)\n",
    "x2observe = np.linspace(ranges[2], ranges[3], 5)\n",
    "x1observe, x2observe = np.meshgrid(x1observe, x2observe, sparse = False, indexing = 'xy')  \n",
    "data = np.vstack([x1observe.ravel(), x2observe.ravel()]).T\n",
    "observations = world.sample_value(data)\n",
    "\n",
    "# Create the point robot\n",
    "robot = Nonmyopic_Robot(sample_world = world.sample_value, \n",
    "              start_loc = (0.0, 0.0, 0.0), \n",
    "              ranges = (-10., 10., -10., 10.),\n",
    "              kernel_file = None,\n",
    "              kernel_dataset = None,\n",
    "              prior_dataset =  None, \n",
    "              init_lengthscale = 3.0, \n",
    "              init_variance = 100.0, \n",
    "              noise = 0.05,\n",
    "              path_generator = 'equal_dubins',\n",
    "              frontier_size = 20, \n",
    "              horizon_length = 5.0, \n",
    "              turning_radius = 0.5, \n",
    "              sample_step = 2.0,\n",
    "              evaluation = evaluation, \n",
    "              f_rew = reward_function,\n",
    "              computation_budget = 2.0,\n",
    "              rollout_length = 3)\n",
    "\n",
    "robot.nonmyopic_planner(T = 150)\n",
    "robot.visualize_world_model()\n",
    "robot.visualize_trajectory()\n",
    "robot.plot_information()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.14"
  }
 },
 "nbformat": 4,
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